COVIID-19 R Example
This R markdown document provides an example of the basic sequence of commands in R to:
- load the needed libraries to perform an analysis
- import data
- reshape and process the data to support an analysis and visualization
- perform an analysis (describe & summarize)
- visualize the data
In this case we will download US COVID-19 infection data for each US county and combine those data with demongraphic data from the US Census Bureau to gain an understanding of per-capita rates of increase in infection relative to some demographic variables of interest.
Import Libraries
knitr::opts_chunk$set(message = FALSE)
options("scipen"=100, "digits"=4) # tune up when numbers will be displayed in fixed vs. scientific notation
library(tidyverse) # core meta-package for a bunch of the tidyverse packages## ── Attaching packages ────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.3
## ✓ tibble 2.1.3 ✓ dplyr 0.8.4
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.4.0## ── Conflicts ───────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(readxl) # read XLS files into a datafram
library(lubridate) # convenience functions for processing dates##
## Attaching package: 'lubridate'## The following object is masked from 'package:base':
##
## datelibrary(ggthemes) # add some great themes to use in our plots
library(RColorBrewer) # add the color brewer color palette
library(knitr) # combined with kableExtra output tibbles as nicely formatted tables
library(kableExtra) # combined with kableExtra output tibbles as nicely formatted tables##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rowsImport data
To perform our analysis we are going to retrieve and import three datasets:
US Census Bureau - American Community Survey 5-year (ACS5) population estimates for all US countys from 2018. https://data.census.gov/cedsci/table?q=population%20by%20county&g=0100000US.050000&hidePreview=true&tid=ACSST1Y2018.S0101&vintage=2018
US Census Bureau - USA Counties 2011 Land Area Data (LAD) and associated reference data. Note that these data are provided as a zipped xls file.
The current US confirmed COVID-19 infection count by county/territory time series data (C19) from the Johns Hopkins CSSE Github repository. https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv
ACS5 data import
These data will be imported from a previously downloaded copy of the data from the US Census Bureau web site (referenced above). These data come in the form of a CSV file and an associated CSV file that describes the content of each of the columns in the file. We will use the Tidyverse read_csv command to import this file from the local data folder.
# relative path and filename for the csv file to be imported
acs5_filepath <- "data/ACS2018/ACSST5Y2018.S0101_data_with_overlays_2020-04-06T234438.csv"
acs5_raw <- read_csv(acs5_filepath, col_names = TRUE, skip = 1)Column specifications
glimpse(acs5_raw)## Observations: 3,220
## Variables: 458
## $ id <chr> …
## $ `Geographic Area Name` <chr> …
## $ `Estimate!!Total!!Total population` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population` <chr> …
## $ `Estimate!!Percent!!Total population` <chr> …
## $ `Margin of Error!!Percent MOE!!Total population` <chr> …
## $ `Estimate!!Male!!Total population` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population` <chr> …
## $ `Estimate!!Percent Male!!Total population` <chr> …
## $ `Margin of Error!!Percent Male MOE!!Total population` <chr> …
## $ `Estimate!!Female!!Total population` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population` <chr> …
## $ `Estimate!!Percent Female!!Total population` <chr> …
## $ `Margin of Error!!Percent Female MOE!!Total population` <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!Under 5 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!Under 5 years` <chr> …
## $ `Estimate!!Percent!!Total population!!AGE!!Under 5 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!Under 5 years` <chr> …
## $ `Estimate!!Male!!Total population!!AGE!!Under 5 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!Under 5 years` <chr> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!Under 5 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!Under 5 years` <chr> …
## $ `Estimate!!Female!!Total population!!AGE!!Under 5 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!Under 5 years` <chr> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!Under 5 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!Under 5 years` <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!5 to 9 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!5 to 9 years` <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!5 to 9 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!5 to 9 years` <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!5 to 9 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!5 to 9 years` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!5 to 9 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!5 to 9 years` <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!5 to 9 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!5 to 9 years` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!5 to 9 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!5 to 9 years` <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!10 to 14 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!10 to 14 years` <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!10 to 14 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!10 to 14 years` <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!10 to 14 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!10 to 14 years` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!10 to 14 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!10 to 14 years` <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!10 to 14 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!10 to 14 years` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!10 to 14 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!10 to 14 years` <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!15 to 19 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!15 to 19 years` <chr> …
## $ `Estimate!!Percent!!Total population!!AGE!!15 to 19 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!15 to 19 years` <chr> …
## $ `Estimate!!Male!!Total population!!AGE!!15 to 19 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!15 to 19 years` <chr> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!15 to 19 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!15 to 19 years` <chr> …
## $ `Estimate!!Female!!Total population!!AGE!!15 to 19 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!15 to 19 years` <chr> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!15 to 19 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!15 to 19 years` <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!20 to 24 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!20 to 24 years` <chr> …
## $ `Estimate!!Percent!!Total population!!AGE!!20 to 24 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!20 to 24 years` <chr> …
## $ `Estimate!!Male!!Total population!!AGE!!20 to 24 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!20 to 24 years` <chr> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!20 to 24 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!20 to 24 years` <chr> …
## $ `Estimate!!Female!!Total population!!AGE!!20 to 24 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!20 to 24 years` <chr> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!20 to 24 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!20 to 24 years` <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!25 to 29 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!25 to 29 years` <chr> …
## $ `Estimate!!Percent!!Total population!!AGE!!25 to 29 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!25 to 29 years` <chr> …
## $ `Estimate!!Male!!Total population!!AGE!!25 to 29 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!25 to 29 years` <chr> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!25 to 29 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!25 to 29 years` <chr> …
## $ `Estimate!!Female!!Total population!!AGE!!25 to 29 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!25 to 29 years` <chr> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!25 to 29 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!25 to 29 years` <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!30 to 34 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!30 to 34 years` <chr> …
## $ `Estimate!!Percent!!Total population!!AGE!!30 to 34 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!30 to 34 years` <chr> …
## $ `Estimate!!Male!!Total population!!AGE!!30 to 34 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!30 to 34 years` <chr> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!30 to 34 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!30 to 34 years` <chr> …
## $ `Estimate!!Female!!Total population!!AGE!!30 to 34 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!30 to 34 years` <chr> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!30 to 34 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!30 to 34 years` <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!35 to 39 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!35 to 39 years` <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!35 to 39 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!35 to 39 years` <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!35 to 39 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!35 to 39 years` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!35 to 39 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!35 to 39 years` <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!35 to 39 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!35 to 39 years` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!35 to 39 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!35 to 39 years` <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!40 to 44 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!40 to 44 years` <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!40 to 44 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!40 to 44 years` <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!40 to 44 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!40 to 44 years` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!40 to 44 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!40 to 44 years` <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!40 to 44 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!40 to 44 years` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!40 to 44 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!40 to 44 years` <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!45 to 49 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!45 to 49 years` <chr> …
## $ `Estimate!!Percent!!Total population!!AGE!!45 to 49 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!45 to 49 years` <chr> …
## $ `Estimate!!Male!!Total population!!AGE!!45 to 49 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!45 to 49 years` <chr> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!45 to 49 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!45 to 49 years` <chr> …
## $ `Estimate!!Female!!Total population!!AGE!!45 to 49 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!45 to 49 years` <chr> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!45 to 49 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!45 to 49 years` <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!50 to 54 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!50 to 54 years` <chr> …
## $ `Estimate!!Percent!!Total population!!AGE!!50 to 54 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!50 to 54 years` <chr> …
## $ `Estimate!!Male!!Total population!!AGE!!50 to 54 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!50 to 54 years` <chr> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!50 to 54 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!50 to 54 years` <chr> …
## $ `Estimate!!Female!!Total population!!AGE!!50 to 54 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!50 to 54 years` <chr> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!50 to 54 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!50 to 54 years` <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!55 to 59 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!55 to 59 years` <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!55 to 59 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!55 to 59 years` <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!55 to 59 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!55 to 59 years` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!55 to 59 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!55 to 59 years` <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!55 to 59 years` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!55 to 59 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!55 to 59 years` <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!60 to 64 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!60 to 64 years` <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!60 to 64 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!60 to 64 years` <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!60 to 64 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!60 to 64 years` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!60 to 64 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!60 to 64 years` <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!60 to 64 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!60 to 64 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!55 to 59 years` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!60 to 64 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!60 to 64 years` <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!65 to 69 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!65 to 69 years` <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!65 to 69 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!65 to 69 years` <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!65 to 69 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!65 to 69 years` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!65 to 69 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!65 to 69 years` <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!65 to 69 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!65 to 69 years` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!65 to 69 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!65 to 69 years` <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!70 to 74 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!70 to 74 years` <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!70 to 74 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!70 to 74 years` <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!70 to 74 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!70 to 74 years` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!70 to 74 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!70 to 74 years` <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!70 to 74 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!70 to 74 years` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!70 to 74 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!70 to 74 years` <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!75 to 79 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!75 to 79 years` <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!75 to 79 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!75 to 79 years` <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!75 to 79 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!75 to 79 years` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!75 to 79 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!75 to 79 years` <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!75 to 79 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!75 to 79 years` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!75 to 79 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!75 to 79 years` <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!80 to 84 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!80 to 84 years` <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!80 to 84 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!80 to 84 years` <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!80 to 84 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!80 to 84 years` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!80 to 84 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!80 to 84 years` <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!80 to 84 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!80 to 84 years` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!80 to 84 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!80 to 84 years` <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!85 years and over` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!85 years and over` <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!85 years and over` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!85 years and over` <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!85 years and over` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!85 years and over` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!85 years and over` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!85 years and over` <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!85 years and over` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!85 years and over` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!85 years and over` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!85 years and over` <dbl> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years` <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years` <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years` <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years` <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years` <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years` <chr> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years` <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years` <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years` <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years` <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years` <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years` <chr> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years` <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years` <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years` <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years` <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years` <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years` <chr> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years` <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years` <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years` <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years` <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years` <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years` <chr> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years` <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years` <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years` <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years` <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years` <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years` <chr> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!16 years and over` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!16 years and over` <dbl> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!16 years and over` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!16 years and over` <dbl> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!16 years and over` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!16 years and over` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!16 years and over` <dbl> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!16 years and over` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!16 years and over` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!16 years and over` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!16 years and over` <dbl> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!18 years and over` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!18 years and over` <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!18 years and over` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!18 years and over` <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!18 years and over` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!18 years and over` <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!18 years and over` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!18 years and over` <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!18 years and over` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!18 years and over` <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!18 years and over` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!18 years and over` <chr> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!21 years and over` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!21 years and over` <dbl> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!21 years and over` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!21 years and over` <dbl> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!21 years and over` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!21 years and over` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!21 years and over` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!21 years and over` <dbl> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!21 years and over` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!21 years and over` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!21 years and over` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!21 years and over` <dbl> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!60 years and over` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!60 years and over` <dbl> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!60 years and over` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!60 years and over` <dbl> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!60 years and over` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!60 years and over` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!60 years and over` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!60 years and over` <dbl> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!60 years and over` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!60 years and over` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!60 years and over` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!60 years and over` <dbl> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!62 years and over` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!62 years and over` <dbl> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!62 years and over` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!62 years and over` <dbl> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!62 years and over` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!62 years and over` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!62 years and over` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!62 years and over` <dbl> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!62 years and over` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!62 years and over` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!62 years and over` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!62 years and over` <dbl> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!65 years and over` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!65 years and over` <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!65 years and over` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!65 years and over` <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!65 years and over` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!65 years and over` <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!65 years and over` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!65 years and over` <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!65 years and over` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!65 years and over` <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!65 years and over` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!65 years and over` <chr> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!75 years and over` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!75 years and over` <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!75 years and over` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!75 years and over` <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!75 years and over` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!75 years and over` <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!75 years and over` <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!75 years and over` <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!75 years and over` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!75 years and over` <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!75 years and over` <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!75 years and over` <chr> …
## $ `Estimate!!Total!!Total population!!SUMMARY INDICATORS!!Median age (years)` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SUMMARY INDICATORS!!Median age (years)` <dbl> …
## $ `Estimate!!Percent!!Total population!!SUMMARY INDICATORS!!Median age (years)` <chr> …
## $ `Margin of Error!!Percent MOE!!Total population!!SUMMARY INDICATORS!!Median age (years)` <chr> …
## $ `Estimate!!Male!!Total population!!SUMMARY INDICATORS!!Median age (years)` <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SUMMARY INDICATORS!!Median age (years)` <dbl> …
## $ `Estimate!!Percent Male!!Total population!!SUMMARY INDICATORS!!Median age (years)` <chr> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SUMMARY INDICATORS!!Median age (years)` <chr> …
## $ `Estimate!!Female!!Total population!!SUMMARY INDICATORS!!Median age (years)` <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SUMMARY INDICATORS!!Median age (years)` <dbl> …
## $ `Estimate!!Percent Female!!Total population!!SUMMARY INDICATORS!!Median age (years)` <chr> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SUMMARY INDICATORS!!Median age (years)` <chr> …
## $ `Estimate!!Total!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)` <chr> …
## $ `Estimate!!Percent!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)` <chr> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!16 years and over` <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)` <chr> …
## $ `Estimate!!Male!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)` <chr> …
## $ `Margin of Error!!Male MOE!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)` <chr> …
## $ `Estimate!!Percent Male!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)` <chr> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)` <chr> …
## $ `Estimate!!Female!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)` <chr> …
## $ `Margin of Error!!Female MOE!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)` <chr> …
## $ `Estimate!!Percent Female!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)` <chr> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)` <chr> …
## $ `Estimate!!Total!!Total population!!SUMMARY INDICATORS!!Age dependency ratio` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SUMMARY INDICATORS!!Age dependency ratio` <chr> …
## $ `Estimate!!Percent!!Total population!!SUMMARY INDICATORS!!Age dependency ratio` <chr> …
## $ `Estimate!!Female!!Total population!!SUMMARY INDICATORS!!Child dependency ratio` <chr> …
## $ `Margin of Error!!Female MOE!!Total population!!SUMMARY INDICATORS!!Child dependency ratio` <chr> …
## $ `Estimate!!Percent Female!!Total population!!SUMMARY INDICATORS!!Child dependency ratio` <chr> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SUMMARY INDICATORS!!Child dependency ratio` <chr> …
## $ `Estimate!!Total!!PERCENT ALLOCATED!!Sex` <chr> …
## $ `Margin of Error!!Total MOE!!PERCENT ALLOCATED!!Sex` <chr> …
## $ `Estimate!!Percent!!PERCENT ALLOCATED!!Sex` <dbl> …
## $ `Margin of Error!!Percent MOE!!PERCENT ALLOCATED!!Sex` <chr> …
## $ `Estimate!!Male!!PERCENT ALLOCATED!!Sex` <chr> …
## $ `Margin of Error!!Male MOE!!PERCENT ALLOCATED!!Sex` <chr> …
## $ `Estimate!!Percent Male!!PERCENT ALLOCATED!!Sex` <chr> …
## $ `Margin of Error!!Percent Male MOE!!PERCENT ALLOCATED!!Sex` <chr> …
## $ `Margin of Error!!Percent MOE!!Total population!!SUMMARY INDICATORS!!Age dependency ratio` <chr> …
## $ `Estimate!!Male!!Total population!!SUMMARY INDICATORS!!Age dependency ratio` <chr> …
## $ `Margin of Error!!Male MOE!!Total population!!SUMMARY INDICATORS!!Age dependency ratio` <chr> …
## $ `Estimate!!Percent Male!!Total population!!SUMMARY INDICATORS!!Age dependency ratio` <chr> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SUMMARY INDICATORS!!Age dependency ratio` <chr> …
## $ `Estimate!!Female!!Total population!!SUMMARY INDICATORS!!Age dependency ratio` <chr> …
## $ `Margin of Error!!Female MOE!!Total population!!SUMMARY INDICATORS!!Age dependency ratio` <chr> …
## $ `Estimate!!Percent Female!!Total population!!SUMMARY INDICATORS!!Age dependency ratio` <chr> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SUMMARY INDICATORS!!Age dependency ratio` <chr> …
## $ `Estimate!!Total!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio` <chr> …
## $ `Estimate!!Percent!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio` <chr> …
## $ `Margin of Error!!Percent MOE!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio` <chr> …
## $ `Estimate!!Male!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio` <chr> …
## $ `Margin of Error!!Male MOE!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio` <chr> …
## $ `Estimate!!Percent Male!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio` <chr> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio` <chr> …
## $ `Estimate!!Female!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio` <chr> …
## $ `Margin of Error!!Female MOE!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio` <chr> …
## $ `Estimate!!Percent Female!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio` <chr> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio` <chr> …
## $ `Estimate!!Total!!Total population!!SUMMARY INDICATORS!!Child dependency ratio` <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SUMMARY INDICATORS!!Child dependency ratio` <chr> …
## $ `Estimate!!Percent!!Total population!!SUMMARY INDICATORS!!Child dependency ratio` <chr> …
## $ `Margin of Error!!Percent MOE!!Total population!!SUMMARY INDICATORS!!Child dependency ratio` <chr> …
## $ `Estimate!!Male!!Total population!!SUMMARY INDICATORS!!Child dependency ratio` <chr> …
## $ `Margin of Error!!Male MOE!!Total population!!SUMMARY INDICATORS!!Child dependency ratio` <chr> …
## $ `Estimate!!Percent Male!!Total population!!SUMMARY INDICATORS!!Child dependency ratio` <chr> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SUMMARY INDICATORS!!Child dependency ratio` <chr> …
## $ `Estimate!!Female!!PERCENT ALLOCATED!!Sex` <chr> …
## $ `Margin of Error!!Female MOE!!PERCENT ALLOCATED!!Sex` <chr> …
## $ `Estimate!!Percent Female!!PERCENT ALLOCATED!!Sex` <chr> …
## $ `Margin of Error!!Percent Female MOE!!PERCENT ALLOCATED!!Sex` <chr> …
## $ `Estimate!!Total!!PERCENT ALLOCATED!!Age` <chr> …
## $ `Margin of Error!!Total MOE!!PERCENT ALLOCATED!!Age` <chr> …
## $ `Estimate!!Percent!!PERCENT ALLOCATED!!Age` <dbl> …
## $ `Margin of Error!!Percent MOE!!PERCENT ALLOCATED!!Age` <chr> …
## $ `Estimate!!Male!!PERCENT ALLOCATED!!Age` <chr> …
## $ `Margin of Error!!Male MOE!!PERCENT ALLOCATED!!Age` <chr> …
## $ `Estimate!!Percent Male!!PERCENT ALLOCATED!!Age` <chr> …
## $ `Margin of Error!!Percent Male MOE!!PERCENT ALLOCATED!!Age` <chr> …
## $ `Estimate!!Female!!PERCENT ALLOCATED!!Age` <chr> …
## $ `Margin of Error!!Female MOE!!PERCENT ALLOCATED!!Age` <chr> …
## $ `Estimate!!Percent Female!!PERCENT ALLOCATED!!Age` <chr> …
## $ `Margin of Error!!Percent Female MOE!!PERCENT ALLOCATED!!Age` <chr> …Import problems
problems(acs5_raw)LAD Data Import
These data will be imported from a previously downloaded copy of the data file that was provided by the US Census Bureau as an XLS file. To read this file we have to have previously loaded the readxl library into our R session.
# relative path and filename for the xls file to be imported
lad_filepath <- "data/LandArea/LND01.xls"
lad_raw <- read_excel(lad_filepath)Column specifications
glimpse(lad_raw)## Observations: 3,198
## Variables: 34
## $ Areaname <chr> "UNITED STATES", "ALABAMA", "Autauga, AL", "Baldwin, AL",…
## $ STCOU <chr> "00000", "01000", "01001", "01003", "01005", "01007", "01…
## $ LND010190F <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND010190D <dbl> 3787425.1, 52422.9, 604.5, 2027.1, 904.6, 625.5, 650.6, 6…
## $ LND010190N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND010190N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND010200F <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND010200D <dbl> 3794083.1, 52419.0, 604.5, 2026.9, 904.5, 626.2, 650.6, 6…
## $ LND010200N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND010200N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110180F <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND110180D <dbl> 3539289.2, 50767.2, 597.0, 1589.4, 883.9, 625.0, 643.3, 6…
## $ LND110180N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110180N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110190F <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND110190D <dbl> 3536341.7, 50750.2, 596.0, 1596.5, 885.0, 622.4, 645.7, 6…
## $ LND110190N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110190N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110200F <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND110200D <dbl> 3537438.4, 50744.0, 596.0, 1596.3, 884.9, 623.0, 645.6, 6…
## $ LND110200N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110200N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110210F <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND110210D <dbl> 3531905.4, 50645.3, 594.4, 1589.8, 884.9, 622.6, 644.8, 6…
## $ LND110210N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110210N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND210190F <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND210190D <dbl> 251083.35, 1672.71, 8.48, 430.55, 19.59, 3.14, 4.97, 1.04…
## $ LND210190N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND210190N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND210200F <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND210200D <dbl> 256644.62, 1675.01, 8.48, 430.58, 19.61, 3.14, 5.02, 1.04…
## $ LND210200N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND210200N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…Import problems
problems(lad_raw)C19 data import
These data are going to be directly downloaded from the github repository that the Johns Hopkins CSSE updates on a regular basis. By redownloading the current data from the repository our analysis will always reflect the current state of knowledge about infections for each county in the database.
# relative path and filename for the xls file to be imported
c19_filepath <- "https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv"
c19_raw <- read_csv(c19_filepath)Column specifications
glimpse(c19_raw)## Observations: 3,340
## Variables: 410
## $ UID <dbl> 84001001, 84001003, 84001005, 84001007, 84001009, 8400…
## $ iso2 <chr> "US", "US", "US", "US", "US", "US", "US", "US", "US", …
## $ iso3 <chr> "USA", "USA", "USA", "USA", "USA", "USA", "USA", "USA"…
## $ code3 <dbl> 840, 840, 840, 840, 840, 840, 840, 840, 840, 840, 840,…
## $ FIPS <dbl> 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 1017, …
## $ Admin2 <chr> "Autauga", "Baldwin", "Barbour", "Bibb", "Blount", "Bu…
## $ Province_State <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",…
## $ Country_Region <chr> "US", "US", "US", "US", "US", "US", "US", "US", "US", …
## $ Lat <dbl> 32.54, 30.73, 31.87, 33.00, 33.98, 32.10, 31.75, 33.77…
## $ Long_ <dbl> -86.64, -87.72, -85.39, -87.13, -86.57, -85.71, -86.68…
## $ Combined_Key <chr> "Autauga, Alabama, US", "Baldwin, Alabama, US", "Barbo…
## $ `1/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/15/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/16/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/17/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/18/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/19/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/20/20` <dbl> 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/21/20` <dbl> 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/22/20` <dbl> 0, 2, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/23/20` <dbl> 0, 3, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/24/20` <dbl> 1, 5, 0, 0, 0, 0, 0, 2, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/25/20` <dbl> 5, 5, 0, 0, 3, 0, 1, 2, 9, 1, 4, 0, 0, 1, 1, 0, 1, 0, …
## $ `3/26/20` <dbl> 6, 6, 0, 0, 4, 1, 1, 2, 13, 1, 4, 1, 0, 1, 1, 0, 1, 0,…
## $ `3/27/20` <dbl> 6, 6, 0, 2, 6, 1, 1, 4, 15, 1, 6, 1, 0, 1, 3, 0, 1, 0,…
## $ `3/28/20` <dbl> 6, 18, 0, 2, 6, 2, 1, 4, 22, 1, 7, 1, 0, 2, 4, 0, 2, 0…
## $ `3/29/20` <dbl> 6, 19, 0, 2, 7, 2, 1, 4, 29, 1, 8, 1, 0, 2, 5, 0, 2, 0…
## $ `3/30/20` <dbl> 8, 22, 0, 3, 7, 2, 1, 9, 39, 1, 10, 2, 0, 2, 5, 0, 4, …
## $ `3/31/20` <dbl> 8, 23, 0, 3, 7, 2, 1, 10, 40, 1, 11, 4, 0, 2, 5, 0, 4,…
## $ `4/1/20` <dbl> 10, 26, 0, 3, 7, 2, 1, 11, 51, 1, 13, 5, 2, 3, 6, 1, 4…
## $ `4/2/20` <dbl> 12, 29, 0, 4, 9, 2, 1, 12, 75, 3, 14, 5, 2, 7, 6, 4, 5…
## $ `4/3/20` <dbl> 12, 31, 2, 4, 11, 2, 1, 22, 87, 4, 15, 5, 3, 8, 7, 7, …
## $ `4/4/20` <dbl> 12, 34, 3, 4, 12, 2, 1, 23, 91, 5, 15, 5, 7, 9, 7, 8, …
## $ `4/5/20` <dbl> 12, 39, 3, 7, 13, 2, 1, 26, 94, 5, 18, 6, 9, 9, 7, 8, …
## $ `4/6/20` <dbl> 12, 43, 4, 7, 13, 2, 1, 40, 102, 5, 20, 8, 9, 9, 9, 9,…
## $ `4/7/20` <dbl> 12, 47, 4, 8, 13, 2, 2, 51, 112, 5, 21, 8, 10, 9, 12, …
## $ `4/8/20` <dbl> 12, 54, 4, 9, 13, 3, 3, 54, 145, 7, 23, 8, 10, 11, 12,…
## $ `4/9/20` <dbl> 17, 63, 8, 11, 15, 4, 3, 56, 170, 7, 26, 8, 13, 11, 12…
## $ `4/10/20` <dbl> 18, 64, 9, 11, 15, 4, 3, 56, 178, 7, 29, 10, 13, 11, 1…
## $ `4/11/20` <dbl> 19, 70, 10, 13, 15, 4, 6, 59, 189, 7, 31, 11, 15, 12, …
## $ `4/12/20` <dbl> 19, 75, 10, 16, 17, 4, 7, 63, 206, 9, 31, 12, 18, 14, …
## $ `4/13/20` <dbl> 19, 82, 10, 17, 18, 6, 8, 64, 215, 9, 35, 12, 18, 14, …
## $ `4/14/20` <dbl> 23, 93, 11, 18, 19, 8, 8, 65, 219, 9, 35, 14, 18, 14, …
## $ `4/15/20` <dbl> 24, 100, 13, 19, 21, 8, 11, 66, 229, 10, 38, 15, 19, 1…
## $ `4/16/20` <dbl> 24, 104, 14, 23, 22, 8, 11, 68, 234, 11, 38, 15, 21, 1…
## $ `4/17/20` <dbl> 24, 106, 15, 23, 22, 8, 13, 68, 239, 11, 39, 15, 21, 1…
## $ `4/18/20` <dbl> 25, 110, 18, 26, 24, 9, 14, 71, 242, 12, 41, 16, 21, 1…
## $ `4/19/20` <dbl> 26, 116, 20, 26, 25, 9, 15, 78, 247, 12, 45, 16, 21, 1…
## $ `4/20/20` <dbl> 28, 120, 22, 30, 27, 11, 15, 82, 257, 13, 45, 18, 22, …
## $ `4/21/20` <dbl> 30, 126, 28, 30, 31, 11, 15, 85, 259, 14, 45, 18, 22, …
## $ `4/22/20` <dbl> 32, 134, 29, 32, 33, 11, 17, 87, 266, 14, 47, 21, 22, …
## $ `4/23/20` <dbl> 33, 143, 31, 32, 35, 12, 19, 89, 273, 14, 49, 23, 23, …
## $ `4/24/20` <dbl> 36, 148, 33, 33, 36, 12, 21, 89, 277, 14, 51, 23, 23, …
## $ `4/25/20` <dbl> 36, 155, 34, 34, 36, 12, 28, 90, 279, 14, 52, 25, 24, …
## $ `4/26/20` <dbl> 36, 161, 34, 37, 38, 12, 32, 91, 281, 15, 53, 30, 25, …
## $ `4/27/20` <dbl> 37, 167, 35, 40, 38, 12, 34, 91, 284, 15, 53, 36, 26, …
## $ `4/28/20` <dbl> 39, 172, 38, 40, 38, 12, 48, 94, 286, 16, 54, 37, 27, …
## $ `4/29/20` <dbl> 41, 174, 38, 40, 39, 12, 53, 95, 288, 16, 54, 37, 30, …
## $ `4/30/20` <dbl> 42, 180, 38, 40, 39, 13, 55, 97, 292, 16, 55, 44, 31, …
## $ `5/1/20` <dbl> 43, 180, 42, 41, 41, 14, 68, 97, 295, 16, 55, 50, 33, …
## $ `5/2/20` <dbl> 47, 185, 43, 41, 42, 14, 93, 102, 296, 16, 56, 50, 37,…
## $ `5/3/20` <dbl> 51, 190, 45, 42, 42, 14, 104, 109, 302, 19, 59, 50, 41…
## $ `5/4/20` <dbl> 54, 190, 45, 43, 42, 15, 113, 109, 307, 19, 61, 54, 41…
## $ `5/5/20` <dbl> 54, 194, 47, 43, 42, 17, 118, 118, 309, 19, 64, 56, 42…
## $ `5/6/20` <dbl> 56, 199, 47, 43, 44, 17, 128, 120, 313, 20, 64, 56, 44…
## $ `5/7/20` <dbl> 58, 207, 50, 44, 46, 17, 152, 126, 317, 20, 69, 57, 48…
## $ `5/8/20` <dbl> 62, 211, 52, 44, 47, 20, 159, 129, 320, 22, 69, 60, 51…
## $ `5/9/20` <dbl> 63, 219, 57, 45, 47, 21, 175, 130, 323, 23, 70, 62, 51…
## $ `5/10/20` <dbl> 72, 224, 59, 46, 47, 22, 188, 130, 325, 23, 72, 67, 55…
## $ `5/11/20` <dbl> 81, 226, 61, 46, 48, 26, 196, 131, 327, 24, 74, 68, 62…
## $ `5/12/20` <dbl> 88, 230, 67, 46, 48, 26, 223, 132, 332, 24, 76, 70, 64…
## $ `5/13/20` <dbl> 90, 237, 70, 46, 48, 27, 231, 133, 333, 24, 76, 73, 65…
## $ `5/14/20` <dbl> 100, 247, 74, 47, 48, 27, 244, 134, 334, 24, 78, 77, 6…
## $ `5/15/20` <dbl> 100, 253, 79, 51, 48, 32, 254, 135, 335, 25, 79, 81, 7…
## $ `5/16/20` <dbl> 108, 261, 81, 52, 49, 35, 265, 136, 337, 26, 81, 84, 7…
## $ `5/17/20` <dbl> 118, 267, 85, 52, 49, 39, 278, 139, 337, 27, 83, 85, 7…
## $ `5/18/20` <dbl> 124, 267, 89, 53, 50, 50, 289, 139, 338, 28, 84, 89, 7…
## $ `5/19/20` <dbl> 130, 269, 92, 53, 50, 58, 297, 142, 339, 30, 85, 124, …
## $ `5/20/20` <dbl> 135, 277, 96, 54, 50, 64, 306, 142, 342, 30, 87, 127, …
## $ `5/21/20` <dbl> 148, 277, 101, 54, 51, 71, 315, 143, 343, 31, 88, 132,…
## $ `5/22/20` <dbl> 151, 278, 106, 56, 52, 95, 325, 145, 343, 32, 89, 135,…
## $ `5/23/20` <dbl> 156, 280, 108, 59, 52, 103, 329, 145, 343, 32, 90, 139…
## $ `5/24/20` <dbl> 160, 281, 113, 60, 52, 109, 338, 148, 349, 32, 91, 140…
## $ `5/25/20` <dbl> 171, 284, 116, 63, 52, 138, 360, 153, 350, 33, 91, 143…
## $ `5/26/20` <dbl> 191, 285, 128, 68, 54, 167, 375, 157, 353, 33, 95, 146…
## $ `5/27/20` <dbl> 192, 289, 133, 72, 56, 175, 387, 159, 356, 33, 99, 146…
## $ `5/28/20` <dbl> 204, 289, 136, 72, 61, 185, 389, 159, 364, 34, 102, 14…
## $ `5/29/20` <dbl> 211, 290, 151, 72, 63, 201, 394, 160, 367, 36, 104, 14…
## $ `5/30/20` <dbl> 216, 292, 155, 74, 65, 205, 403, 161, 369, 37, 104, 15…
## $ `5/31/20` <dbl> 227, 298, 172, 77, 66, 209, 413, 169, 370, 38, 105, 15…
## $ `6/1/20` <dbl> 237, 299, 177, 78, 66, 211, 414, 172, 373, 38, 106, 15…
## $ `6/2/20` <dbl> 239, 301, 181, 78, 66, 215, 418, 172, 374, 38, 108, 15…
## $ `6/3/20` <dbl> 241, 302, 181, 78, 66, 217, 420, 174, 375, 38, 111, 15…
## $ `6/4/20` <dbl> 248, 305, 187, 78, 67, 219, 430, 176, 379, 38, 112, 15…
## $ `6/5/20` <dbl> 259, 312, 194, 79, 73, 225, 441, 181, 389, 40, 112, 15…
## $ `6/6/20` <dbl> 264, 321, 196, 79, 76, 231, 447, 183, 393, 42, 115, 15…
## $ `6/7/20` <dbl> 271, 327, 200, 81, 78, 237, 453, 184, 398, 42, 115, 15…
## $ `6/8/20` <dbl> 282, 332, 201, 87, 80, 242, 462, 186, 407, 42, 121, 15…
## $ `6/9/20` <dbl> 295, 338, 210, 91, 84, 247, 469, 188, 418, 43, 124, 15…
## $ `6/10/20` <dbl> 315, 350, 215, 95, 92, 252, 482, 190, 434, 43, 127, 16…
## $ `6/11/20` <dbl> 323, 357, 220, 97, 98, 256, 498, 193, 443, 46, 128, 16…
## $ `6/12/20` <dbl> 334, 365, 226, 102, 106, 275, 517, 197, 457, 48, 134, …
## $ `6/13/20` <dbl> 361, 367, 233, 106, 114, 300, 534, 201, 474, 52, 138, …
## $ `6/14/20` <dbl> 369, 375, 237, 113, 120, 305, 542, 202, 486, 52, 143, …
## $ `6/15/20` <dbl> 371, 377, 243, 113, 121, 306, 545, 203, 488, 52, 143, …
## $ `6/16/20` <dbl> 377, 379, 252, 116, 125, 310, 548, 206, 491, 53, 151, …
## $ `6/17/20` <dbl> 404, 389, 264, 119, 135, 316, 560, 208, 503, 54, 154, …
## $ `6/18/20` <dbl> 415, 402, 267, 125, 140, 317, 562, 208, 507, 57, 157, …
## $ `6/19/20` <dbl> 435, 408, 272, 125, 144, 324, 563, 209, 515, 57, 162, …
## $ `6/20/20` <dbl> 438, 418, 272, 126, 151, 324, 567, 211, 520, 57, 165, …
## $ `6/21/20` <dbl> 447, 426, 277, 128, 155, 325, 569, 213, 527, 57, 170, …
## $ `6/22/20` <dbl> 458, 439, 280, 134, 162, 327, 572, 214, 533, 57, 176, …
## $ `6/23/20` <dbl> 474, 453, 288, 141, 170, 330, 574, 223, 540, 58, 184, …
## $ `6/24/20` <dbl> 480, 466, 305, 148, 177, 339, 580, 233, 547, 62, 184, …
## $ `6/25/20` <dbl> 493, 505, 313, 161, 186, 340, 586, 236, 558, 66, 192, …
## $ `6/26/20` <dbl> 500, 549, 317, 164, 192, 346, 593, 245, 560, 67, 199, …
## $ `6/27/20` <dbl> 505, 573, 317, 164, 192, 346, 595, 245, 560, 69, 200, …
## $ `6/28/20` <dbl> 528, 628, 324, 167, 200, 354, 598, 261, 581, 72, 208, …
## $ `6/29/20` <dbl> 538, 668, 327, 172, 209, 354, 599, 276, 590, 73, 211, …
## $ `6/30/20` <dbl> 554, 691, 328, 176, 218, 356, 599, 278, 594, 76, 218, …
## $ `7/1/20` <dbl> 562, 739, 337, 181, 221, 358, 602, 288, 609, 82, 221, …
## $ `7/2/20` <dbl> 570, 831, 352, 191, 232, 363, 614, 330, 620, 89, 234, …
## $ `7/3/20` <dbl> 584, 847, 354, 193, 236, 363, 615, 341, 631, 89, 244, …
## $ `7/4/20` <dbl> 601, 861, 356, 196, 240, 363, 624, 363, 634, 101, 251,…
## $ `7/5/20` <dbl> 620, 893, 358, 198, 249, 364, 624, 375, 643, 102, 258,…
## $ `7/6/20` <dbl> 623, 979, 365, 202, 257, 365, 625, 390, 652, 106, 264,…
## $ `7/7/20` <dbl> 656, 1042, 372, 204, 270, 366, 630, 412, 658, 115, 281…
## $ `7/8/20` <dbl> 663, 1118, 377, 214, 289, 366, 632, 443, 672, 115, 292…
## $ `7/9/20` <dbl> 670, 1175, 387, 219, 301, 372, 639, 460, 678, 116, 300…
## $ `7/10/20` <dbl> 691, 1215, 404, 226, 316, 373, 645, 496, 690, 126, 329…
## $ `7/11/20` <dbl> 708, 1280, 409, 229, 338, 373, 646, 520, 694, 128, 337…
## $ `7/12/20` <dbl> 736, 1349, 419, 232, 354, 374, 650, 562, 703, 135, 353…
## $ `7/13/20` <dbl> 749, 1396, 437, 237, 368, 376, 651, 580, 713, 139, 364…
## $ `7/14/20` <dbl> 764, 1506, 446, 243, 390, 377, 655, 649, 719, 144, 377…
## $ `7/15/20` <dbl> 785, 1588, 462, 248, 426, 380, 659, 663, 732, 149, 394…
## $ `7/16/20` <dbl> 797, 1675, 466, 255, 445, 383, 662, 706, 743, 155, 413…
## $ `7/17/20` <dbl> 822, 1806, 483, 262, 464, 389, 668, 728, 756, 160, 431…
## $ `7/18/20` <dbl> 850, 1925, 497, 270, 487, 390, 676, 772, 762, 171, 469…
## $ `7/19/20` <dbl> 862, 1996, 504, 277, 510, 392, 678, 813, 767, 177, 482…
## $ `7/20/20` <dbl> 872, 2085, 513, 283, 526, 393, 685, 849, 774, 181, 498…
## $ `7/21/20` <dbl> 885, 2169, 520, 288, 549, 396, 693, 870, 781, 183, 516…
## $ `7/22/20` <dbl> 905, 2344, 531, 290, 582, 401, 699, 893, 789, 192, 538…
## $ `7/23/20` <dbl> 918, 2482, 540, 304, 616, 405, 705, 974, 796, 205, 563…
## $ `7/24/20` <dbl> 939, 2610, 552, 318, 639, 410, 712, 1025, 810, 207, 58…
## $ `7/25/20` <dbl> 953, 2675, 563, 324, 654, 411, 718, 1086, 820, 209, 60…
## $ `7/26/20` <dbl> 971, 2733, 570, 335, 674, 412, 724, 1163, 824, 220, 62…
## $ `7/27/20` <dbl> 988, 2800, 576, 339, 679, 425, 728, 1206, 835, 221, 64…
## $ `7/28/20` <dbl> 995, 2873, 585, 345, 701, 427, 734, 1260, 844, 227, 66…
## $ `7/29/20` <dbl> 1006, 3001, 586, 353, 738, 431, 740, 1305, 848, 235, 6…
## $ `7/30/20` <dbl> 1029, 3075, 598, 364, 773, 433, 746, 1412, 859, 238, 6…
## $ `7/31/20` <dbl> 1042, 3116, 603, 369, 799, 439, 749, 1445, 862, 242, 7…
## $ `8/1/20` <dbl> 1064, 3194, 611, 373, 820, 441, 756, 1541, 869, 254, 7…
## $ `8/2/20` <dbl> 1078, 3241, 613, 383, 838, 442, 758, 1570, 876, 262, 7…
## $ `8/3/20` <dbl> 1086, 3298, 615, 390, 841, 445, 758, 1608, 882, 268, 7…
## $ `8/4/20` <dbl> 1086, 3347, 616, 393, 845, 448, 761, 1645, 887, 270, 7…
## $ `8/5/20` <dbl> 1109, 3409, 621, 420, 883, 454, 763, 1704, 894, 281, 7…
## $ `8/6/20` <dbl> 1126, 3473, 627, 424, 917, 458, 766, 1744, 900, 291, 8…
## $ `8/7/20` <dbl> 1145, 3533, 629, 434, 929, 467, 766, 1795, 905, 298, 8…
## $ `8/8/20` <dbl> 1175, 3575, 632, 446, 945, 468, 771, 1821, 906, 301, 8…
## $ `8/9/20` <dbl> 1186, 3676, 633, 450, 957, 470, 774, 1847, 909, 302, 8…
## $ `8/10/20` <dbl> 1200, 3700, 642, 454, 971, 486, 775, 1874, 915, 306, 8…
## $ `8/11/20` <dbl> 1224, 3749, 648, 465, 979, 497, 778, 1902, 918, 311, 8…
## $ `8/12/20` <dbl> 1229, 3783, 653, 469, 994, 497, 780, 1925, 919, 319, 8…
## $ `8/13/20` <dbl> 1235, 3825, 658, 475, 998, 498, 786, 1941, 922, 326, 8…
## $ `8/14/20` <dbl> 1245, 3881, 662, 479, 1004, 500, 796, 1980, 925, 333, …
## $ `8/15/20` <dbl> 1252, 3923, 672, 481, 1014, 500, 799, 2000, 927, 337, …
## $ `8/16/20` <dbl> 1258, 3936, 673, 485, 1023, 501, 803, 2015, 928, 341, …
## $ `8/17/20` <dbl> 1276, 3959, 675, 487, 1060, 512, 803, 2061, 937, 350, …
## $ `8/18/20` <dbl> 1281, 3994, 682, 501, 1086, 530, 805, 2121, 943, 351, …
## $ `8/19/20` <dbl> 1293, 4029, 692, 507, 1113, 535, 809, 2181, 949, 358, …
## $ `8/20/20` <dbl> 1304, 4058, 694, 512, 1133, 536, 811, 2211, 965, 362, …
## $ `8/21/20` <dbl> 1316, 4100, 710, 515, 1147, 539, 813, 2246, 969, 367, …
## $ `8/22/20` <dbl> 1318, 4132, 712, 521, 1149, 539, 815, 2260, 970, 369, …
## $ `8/23/20` <dbl> 1337, 4148, 715, 521, 1160, 540, 816, 2285, 970, 374, …
## $ `8/24/20` <dbl> 1343, 4171, 720, 522, 1177, 540, 817, 2302, 973, 376, …
## $ `8/25/20` <dbl> 1357, 4247, 728, 526, 1224, 541, 824, 2360, 982, 379, …
## $ `8/26/20` <dbl> 1365, 4296, 736, 530, 1239, 542, 827, 2380, 1012, 387,…
## $ `8/27/20` <dbl> 1375, 4330, 741, 532, 1251, 544, 829, 2409, 1015, 390,…
## $ `8/28/20` <dbl> 1391, 4408, 749, 539, 1273, 551, 845, 2468, 1023, 395,…
## $ `8/29/20` <dbl> 1424, 4502, 752, 550, 1301, 555, 855, 2500, 1024, 402,…
## $ `8/30/20` <dbl> 1429, 4519, 752, 553, 1312, 556, 857, 2530, 1029, 403,…
## $ `8/31/20` <dbl> 1440, 4538, 759, 558, 1332, 564, 865, 2582, 1037, 414,…
## $ `9/1/20` <dbl> 1442, 4563, 763, 562, 1336, 566, 867, 2606, 1040, 419,…
## $ `9/2/20` <dbl> 1454, 4599, 765, 564, 1356, 567, 868, 2648, 1041, 426,…
## $ `9/3/20` <dbl> 1462, 4626, 770, 567, 1376, 567, 872, 2714, 1047, 440,…
## $ `9/4/20` <dbl> 1474, 4654, 770, 571, 1392, 567, 875, 2786, 1053, 447,…
## $ `9/5/20` <dbl> 1477, 4684, 771, 576, 1399, 569, 879, 2797, 1055, 449,…
## $ `9/6/20` <dbl> 1488, 4700, 772, 578, 1416, 569, 881, 2836, 1058, 452,…
## $ `9/7/20` <dbl> 1494, 4725, 772, 585, 1418, 569, 881, 2840, 1058, 452,…
## $ `9/8/20` <dbl> 1505, 4755, 772, 586, 1424, 569, 882, 2859, 1061, 455,…
## $ `9/9/20` <dbl> 1526, 4796, 779, 589, 1441, 571, 883, 2900, 1069, 465,…
## $ `9/10/20` <dbl> 1530, 4845, 781, 593, 1453, 571, 884, 2922, 1079, 471,…
## $ `9/11/20` <dbl> 1543, 4881, 787, 594, 1459, 572, 886, 2975, 1085, 482,…
## $ `9/12/20` <dbl> 1551, 4915, 789, 596, 1472, 578, 888, 3023, 1085, 502,…
## $ `9/13/20` <dbl> 1567, 4934, 796, 599, 1483, 578, 889, 3040, 1087, 504,…
## $ `9/14/20` <dbl> 1586, 4949, 801, 600, 1490, 579, 890, 3066, 1092, 506,…
## $ `9/15/20` <dbl> 1601, 4964, 807, 601, 1504, 581, 892, 3106, 1097, 510,…
## $ `9/16/20` <dbl> 1614, 4982, 807, 606, 1515, 583, 893, 3156, 1097, 513,…
## $ `9/17/20` <dbl> 1650, 4994, 822, 607, 1538, 583, 896, 3189, 1109, 522,…
## $ `9/18/20` <dbl> 1659, 5016, 825, 619, 1551, 584, 897, 3268, 1114, 539,…
## $ `9/19/20` <dbl> 1675, 5029, 831, 623, 1564, 586, 897, 3289, 1121, 549,…
## $ `9/20/20` <dbl> 1676, 5053, 833, 624, 1573, 590, 898, 3321, 1123, 552,…
## $ `9/21/20` <dbl> 1697, 5090, 846, 628, 1586, 592, 898, 3341, 1131, 557,…
## $ `9/22/20` <dbl> 1697, 5106, 848, 633, 1593, 596, 899, 3353, 1135, 567,…
## $ `9/23/20` <dbl> 1711, 5127, 852, 637, 1605, 597, 901, 3412, 1142, 574,…
## $ `9/24/20` <dbl> 1736, 5397, 868, 646, 1614, 598, 903, 3450, 1151, 586,…
## $ `9/25/20` <dbl> 1750, 5419, 878, 649, 1619, 603, 906, 3466, 1154, 592,…
## $ `9/26/20` <dbl> 1758, 5465, 882, 650, 1623, 605, 907, 3485, 1157, 596,…
## $ `9/27/20` <dbl> 1770, 5524, 883, 651, 1624, 606, 908, 3498, 1161, 597,…
## $ `9/28/20` <dbl> 1776, 5550, 883, 653, 1626, 606, 911, 3516, 1164, 601,…
## $ `9/29/20` <dbl> 1785, 5592, 892, 660, 1633, 608, 913, 3530, 1170, 605,…
## $ `9/30/20` <dbl> 1792, 5954, 894, 668, 1636, 611, 915, 3556, 1172, 609,…
## $ `10/1/20` <dbl> 1799, 5981, 900, 671, 1644, 611, 917, 3597, 1175, 616,…
## $ `10/2/20` <dbl> 1812, 6009, 917, 675, 1656, 611, 919, 3629, 1195, 625,…
## $ `10/3/20` <dbl> 1821, 6034, 917, 683, 1659, 611, 920, 3647, 1196, 625,…
## $ `10/4/20` <dbl> 1824, 6045, 917, 683, 1664, 611, 921, 3672, 1199, 628,…
## $ `10/5/20` <dbl> 1832, 6075, 918, 688, 1667, 614, 923, 3700, 1201, 630,…
## $ `10/6/20` <dbl> 1843, 6103, 923, 700, 1678, 616, 926, 3718, 1203, 636,…
## $ `10/7/20` <dbl> 1847, 6114, 925, 703, 1686, 618, 928, 3736, 1212, 640,…
## $ `10/8/20` <dbl> 1875, 6144, 937, 715, 1700, 622, 937, 3790, 1231, 648,…
## $ `10/9/20` <dbl> 1894, 6164, 940, 724, 1712, 623, 948, 3811, 1235, 653,…
## $ `10/10/20` <dbl> 1901, 6176, 940, 734, 1720, 624, 954, 3829, 1235, 657,…
## $ `10/11/20` <dbl> 1907, 6192, 941, 736, 1732, 625, 958, 3848, 1238, 661,…
## $ `10/12/20` <dbl> 1921, 6222, 948, 741, 1755, 626, 964, 3884, 1247, 674,…
## $ `10/13/20` <dbl> 1925, 6247, 948, 742, 1765, 628, 974, 3911, 1251, 677,…
## $ `10/14/20` <dbl> 1946, 6266, 963, 759, 1782, 628, 979, 3939, 1255, 687,…
## $ `10/15/20` <dbl> 1958, 6313, 966, 769, 1796, 630, 985, 3992, 1258, 694,…
## $ `10/16/20` <dbl> 1971, 6332, 975, 773, 1822, 633, 987, 4053, 1261, 705,…
## $ `10/17/20` <dbl> 1985, 6350, 978, 783, 1837, 633, 993, 4071, 1261, 706,…
## $ `10/18/20` <dbl> 1995, 6356, 978, 787, 1848, 634, 996, 4090, 1268, 707,…
## $ `10/19/20` <dbl> 2006, 6384, 984, 789, 1863, 635, 996, 4115, 1298, 714,…
## $ `10/20/20` <dbl> 2018, 6425, 993, 799, 1887, 636, 996, 4143, 1328, 717,…
## $ `10/21/20` <dbl> 2021, 6459, 1007, 809, 1907, 636, 999, 4175, 1334, 720…
## $ `10/22/20` <dbl> 2027, 6599, 1010, 823, 1923, 638, 1000, 4213, 1341, 72…
## $ `10/23/20` <dbl> 2040, 6619, 1028, 825, 1934, 647, 1005, 4553, 1346, 72…
## $ `10/24/20` <dbl> 2055, 6642, 1030, 839, 1947, 648, 1009, 4587, 1347, 73…
## $ `10/25/20` <dbl> 2070, 6677, 1030, 841, 1958, 648, 1011, 4602, 1349, 73…
## $ `10/26/20` <dbl> 2079, 6694, 1038, 849, 1986, 649, 1011, 4634, 1365, 74…
## $ `10/27/20` <dbl> 2098, 6728, 1042, 858, 2002, 649, 1014, 4675, 1369, 75…
## $ `10/28/20` <dbl> 2120, 6757, 1052, 862, 2027, 650, 1018, 4757, 1379, 75…
## $ `10/29/20` <dbl> 2134, 6879, 1053, 867, 2054, 651, 1018, 4800, 1381, 76…
## $ `10/30/20` <dbl> 2154, 6931, 1058, 873, 2089, 651, 1021, 4855, 1389, 77…
## $ `10/31/20` <dbl> 2168, 6955, 1059, 877, 2109, 653, 1023, 4883, 1392, 78…
## $ `11/1/20` <dbl> 2182, 6974, 1062, 883, 2128, 653, 1025, 4913, 1397, 78…
## $ `11/2/20` <dbl> 2195, 6991, 1073, 890, 2178, 656, 1028, 4946, 1428, 80…
## $ `11/3/20` <dbl> 2210, 7054, 1077, 900, 2204, 658, 1032, 4996, 1449, 81…
## $ `11/4/20` <dbl> 2229, 7093, 1079, 907, 2233, 660, 1035, 5030, 1461, 83…
## $ `11/5/20` <dbl> 2244, 7133, 1089, 920, 2258, 662, 1043, 5072, 1469, 83…
## $ `11/6/20` <dbl> 2257, 7184, 1092, 926, 2290, 663, 1045, 5146, 1483, 83…
## $ `11/7/20` <dbl> 2286, 7226, 1095, 934, 2302, 664, 1051, 5179, 1485, 84…
## $ `11/8/20` <dbl> 2307, 7263, 1098, 942, 2338, 664, 1053, 5214, 1488, 84…
## $ `11/9/20` <dbl> 2328, 7345, 1107, 948, 2378, 665, 1061, 5246, 1506, 85…
## $ `11/10/20` <dbl> 2328, 7348, 1107, 948, 2378, 665, 1061, 5254, 1507, 85…
## $ `11/11/20` <dbl> 2351, 7409, 1112, 961, 2400, 668, 1062, 5282, 1508, 86…
## $ `11/12/20` <dbl> 2385, 7454, 1113, 966, 2429, 669, 1062, 5345, 1514, 87…
## $ `11/13/20` <dbl> 2417, 7523, 1117, 973, 2488, 673, 1068, 5429, 1545, 89…
## $ `11/14/20` <dbl> 2435, 7596, 1123, 978, 2518, 675, 1075, 5470, 1556, 89…
## $ `11/15/20` <dbl> 2456, 7646, 1128, 986, 2549, 677, 1087, 5608, 1570, 90…
## $ `11/16/20` <dbl> 2481, 7696, 1130, 993, 2574, 677, 1095, 5666, 1572, 91…
## $ `11/17/20` <dbl> 2506, 7772, 1134, 1004, 2594, 678, 1099, 5702, 1595, 9…
## $ `11/18/20` <dbl> 2529, 7849, 1137, 1008, 2648, 678, 1102, 5764, 1620, 9…
## $ `11/19/20` <dbl> 2554, 7933, 1145, 1011, 2683, 680, 1113, 5814, 1641, 9…
## $ `11/20/20` <dbl> 2580, 8038, 1151, 1024, 2704, 684, 1120, 5896, 1663, 9…
## $ `11/21/20` <dbl> 2597, 8131, 1157, 1036, 2735, 688, 1132, 5924, 1669, 9…
## $ `11/22/20` <dbl> 2617, 8199, 1160, 1136, 2754, 689, 1133, 5964, 1675, 9…
## $ `11/23/20` <dbl> 2634, 8269, 1161, 1142, 2763, 690, 1137, 5997, 1680, 9…
## $ `11/24/20` <dbl> 2661, 8376, 1167, 1157, 2822, 690, 1143, 6049, 1714, 9…
## $ `11/25/20` <dbl> 2686, 8473, 1170, 1162, 2855, 691, 1144, 6112, 1737, 1…
## $ `11/26/20` <dbl> 2704, 8576, 1170, 1170, 2879, 694, 1153, 6215, 1764, 1…
## $ `11/27/20` <dbl> 2716, 8603, 1171, 1173, 2888, 694, 1153, 6240, 1765, 1…
## $ `11/28/20` <dbl> 2735, 8733, 1173, 1179, 2922, 696, 1165, 6301, 1768, 1…
## $ `11/29/20` <dbl> 2751, 8820, 1175, 1188, 2946, 700, 1173, 6366, 1772, 1…
## $ `11/30/20` <dbl> 2780, 8890, 1178, 1196, 2997, 702, 1178, 6430, 1779, 1…
## $ `12/1/20` <dbl> 2818, 9051, 1189, 1204, 3061, 701, 1186, 6598, 1827, 1…
## $ `12/2/20` <dbl> 2873, 9163, 1206, 1239, 3100, 709, 1188, 6695, 1859, 1…
## $ `12/3/20` <dbl> 2893, 9341, 1214, 1252, 3158, 709, 1200, 6809, 1875, 1…
## $ `12/4/20` <dbl> 2945, 9501, 1217, 1270, 3231, 711, 1211, 6939, 1891, 1…
## $ `12/5/20` <dbl> 2979, 9626, 1219, 1283, 3281, 713, 1225, 7027, 1901, 1…
## $ `12/6/20` <dbl> 3005, 9728, 1223, 1293, 3299, 713, 1236, 7096, 1906, 1…
## $ `12/7/20` <dbl> 3043, 9821, 1224, 1299, 3324, 714, 1244, 7165, 1915, 1…
## $ `12/8/20` <dbl> 3087, 9974, 1240, 1317, 3426, 719, 1257, 7300, 1945, 1…
## $ `12/9/20` <dbl> 3117, 10087, 1245, 1322, 3496, 722, 1263, 7392, 1961, …
## $ `12/10/20` <dbl> 3186, 10288, 1258, 1359, 3600, 722, 1287, 7534, 1977, …
## $ `12/11/20` <dbl> 3233, 10489, 1264, 1398, 3663, 723, 1289, 7658, 1982, …
## $ `12/12/20` <dbl> 3258, 10665, 1269, 1417, 3744, 725, 1306, 7760, 1997, …
## $ `12/13/20` <dbl> 3300, 10806, 1272, 1441, 3776, 728, 1330, 7813, 2013, …
## $ `12/14/20` <dbl> 3329, 10898, 1275, 1455, 3803, 728, 1340, 7872, 2022, …
## $ `12/15/20` <dbl> 3426, 11061, 1292, 1504, 3881, 733, 1332, 7966, 2040, …
## $ `12/16/20` <dbl> 3510, 11212, 1296, 1520, 3950, 737, 1343, 8072, 2064, …
## $ `12/17/20` <dbl> 3570, 11364, 1309, 1548, 4036, 742, 1368, 8290, 2076, …
## $ `12/18/20` <dbl> 3647, 11556, 1318, 1577, 4118, 747, 1384, 8459, 2090, …
## $ `12/19/20` <dbl> 3698, 11722, 1330, 1601, 4191, 752, 1393, 8594, 2116, …
## $ `12/20/20` <dbl> 3741, 11827, 1336, 1613, 4218, 753, 1399, 8648, 2125, …
## $ `12/21/20` <dbl> 3780, 11952, 1336, 1628, 4234, 754, 1405, 8684, 2133, …
## $ `12/22/20` <dbl> 3841, 12155, 1363, 1660, 4313, 760, 1412, 8856, 2161, …
## $ `12/23/20` <dbl> 3889, 12321, 1383, 1683, 4367, 765, 1423, 8968, 2176, …
## $ `12/24/20` <dbl> 3942, 12521, 1390, 1711, 4405, 770, 1434, 9071, 2191, …
## $ `12/25/20` <dbl> 3990, 12666, 1396, 1725, 4441, 777, 1448, 9167, 2200, …
## $ `12/26/20` <dbl> 3999, 12708, 1398, 1739, 4446, 825, 1446, 9198, 2203, …
## $ `12/27/20` <dbl> 4029, 12825, 1406, 1746, 4465, 827, 1452, 9232, 2214, …
## $ `12/28/20` <dbl> 4065, 12962, 1417, 1762, 4483, 830, 1457, 9286, 2229, …
## $ `12/29/20` <dbl> 4105, 13172, 1462, 1792, 4535, 834, 1482, 9345, 2275, …
## $ `12/30/20` <dbl> 4164, 13392, 1492, 1817, 4584, 846, 1493, 9428, 2310, …
## $ `12/31/20` <dbl> 4190, 13601, 1514, 1834, 4641, 859, 1508, 9494, 2341, …
## $ `1/1/21` <dbl> 4239, 13823, 1517, 1854, 4693, 888, 1522, 9584, 2366, …
## $ `1/2/21` <dbl> 4268, 13955, 1528, 1863, 4729, 892, 1530, 9692, 2386, …
## $ `1/3/21` <dbl> 4305, 14064, 1530, 1882, 4746, 900, 1546, 9731, 2402, …
## $ `1/4/21` <dbl> 4336, 14187, 1533, 1885, 4771, 910, 1554, 9752, 2415, …
## $ `1/5/21` <dbl> 4546, 14440, 1575, 1923, 4849, 920, 1574, 9975, 2474, …
## $ `1/6/21` <dbl> 4645, 14656, 1597, 1944, 4898, 925, 1583, 10109, 2519,…
## $ `1/7/21` <dbl> 4705, 14845, 1614, 1981, 4957, 927, 1598, 10283, 2552,…
## $ `1/8/21` <dbl> 4770, 15052, 1634, 2015, 5018, 949, 1610, 10372, 2592,…
## $ `1/9/21` <dbl> 4847, 15202, 1648, 2038, 5047, 950, 1625, 10453, 2620,…
## $ `1/10/21` <dbl> 4879, 15327, 1658, 2051, 5066, 953, 1632, 10497, 2639,…
## $ `1/11/21` <dbl> 4902, 15417, 1663, 2060, 5080, 957, 1637, 10537, 2651,…
## $ `1/12/21` <dbl> 4970, 15572, 1679, 2090, 5134, 967, 1649, 10668, 2697,…
## $ `1/13/21` <dbl> 4998, 15701, 1685, 2109, 5170, 966, 1651, 10745, 2734,…
## $ `1/14/21` <dbl> 5075, 15841, 1696, 2113, 5219, 971, 1669, 10863, 2757,…
## $ `1/15/21` <dbl> 5103, 16002, 1712, 2130, 5264, 981, 1679, 10982, 2778,…
## $ `1/16/21` <dbl> 5154, 16176, 1723, 2144, 5292, 987, 1684, 11078, 2818,…
## $ `1/17/21` <dbl> 5184, 16251, 1729, 2151, 5304, 990, 1696, 11122, 2827,…
## $ `1/18/21` <dbl> 5198, 16346, 1730, 2162, 5308, 991, 1702, 11161, 2842,…
## $ `1/19/21` <dbl> 5227, 16513, 1738, 2170, 5320, 997, 1707, 11206, 2886,…
## $ `1/20/21` <dbl> 5257, 16653, 1760, 2188, 5376, 1011, 1708, 11292, 2931…
## $ `1/21/21` <dbl> 5270, 16798, 1778, 2198, 5411, 1014, 1713, 11365, 2973…
## $ `1/22/21` <dbl> 5327, 16981, 1793, 2212, 5439, 1022, 1724, 11441, 3011…
## $ `1/23/21` <dbl> 5358, 17128, 1805, 2223, 5462, 1033, 1731, 11496, 3034…
## $ `1/24/21` <dbl> 5376, 17256, 1827, 2223, 5473, 1035, 1744, 11521, 3042…
## $ `1/25/21` <dbl> 5407, 17333, 1834, 2229, 5485, 1046, 1748, 11555, 3054…
## $ `1/26/21` <dbl> 5440, 17496, 1882, 2247, 5517, 1058, 1759, 11626, 3085…
## $ `1/27/21` <dbl> 5499, 17629, 1898, 2261, 5568, 1074, 1766, 11730, 3137…
## $ `1/28/21` <dbl> 5554, 17779, 1920, 2271, 5612, 1079, 1788, 11833, 3159…
## $ `1/29/21` <dbl> 5596, 17922, 1931, 2284, 5655, 1075, 1800, 11918, 3174…
## $ `1/30/21` <dbl> 5596, 17922, 1931, 2284, 5655, 1075, 1800, 11918, 3174…
## $ `1/31/21` <dbl> 5669, 18126, 1951, 2307, 5713, 1086, 1812, 12011, 3203…
## $ `2/1/21` <dbl> 5683, 18211, 1956, 2309, 5720, 1089, 1827, 12062, 3210…
## $ `2/2/21` <dbl> 5723, 18344, 1966, 2319, 5745, 1087, 1833, 12102, 3219…
## $ `2/3/21` <dbl> 5753, 18418, 1981, 2321, 5768, 1093, 1838, 12179, 3233…
## $ `2/4/21` <dbl> 5811, 18494, 1989, 2327, 5842, 1107, 1847, 12253, 3239…
## $ `2/5/21` <dbl> 5824, 18568, 1994, 2331, 5871, 1113, 1853, 12325, 3249…
## $ `2/6/21` <dbl> 5856, 18668, 2002, 2334, 5908, 1121, 1863, 12368, 3259…
## $ `2/7/21` <dbl> 5869, 18723, 2008, 2339, 5915, 1128, 1865, 12402, 3263…
## $ `2/8/21` <dbl> 5881, 18763, 2008, 2346, 5920, 1132, 1868, 12426, 3266…
## $ `2/9/21` <dbl> 5910, 18824, 2019, 2362, 5929, 1132, 1872, 12477, 3283…
## $ `2/10/21` <dbl> 5930, 18888, 2024, 2368, 5937, 1131, 1882, 12498, 3291…
## $ `2/11/21` <dbl> 5970, 18960, 2030, 2377, 5955, 1136, 1886, 12539, 3305…
## $ `2/12/21` <dbl> 5984, 18994, 2036, 2385, 5953, 1137, 1892, 12577, 3313…
## $ `2/13/21` <dbl> 6002, 19051, 2040, 2393, 5957, 1139, 1898, 12629, 3318…
## $ `2/14/21` <dbl> 6023, 19105, 2042, 2395, 5961, 1142, 1902, 12700, 3321…
## $ `2/15/21` <dbl> 6024, 19136, 2044, 2397, 5973, 1142, 1905, 12725, 3325…
## $ `2/16/21` <dbl> 6038, 19176, 2055, 2400, 5987, 1145, 1910, 12756, 3336…
## $ `2/17/21` <dbl> 6050, 19267, 2053, 2399, 5997, 1143, 1924, 12784, 3338…
## $ `2/18/21` <dbl> 6071, 19324, 2057, 2405, 6008, 1144, 1930, 12833, 3348…
## $ `2/19/21` <dbl> 6079, 19361, 2061, 2411, 6021, 1147, 1934, 12860, 3358…
## $ `2/20/21` <dbl> 6092, 19392, 2067, 2414, 6040, 1149, 1938, 12915, 3364…
## $ `2/21/21` <dbl> 6117, 19433, 2070, 2416, 6042, 1151, 1940, 12940, 3367…
## $ `2/22/21` <dbl> 6121, 19461, 2074, 2417, 6043, 1153, 1945, 13017, 3367…
## $ `2/23/21` <dbl> 6143, 19554, 2084, 2432, 6058, 1160, 1948, 13063, 3382…Import problems
problems(c19_raw)Reshape and process the data to support an analysis and visualization
In this section of the analysis we are going to extract a useful subset of columns from our three datasets and generate derived datasets that can be used for further analysis and visualization. In support of this activity we are going to illustrate the use of several tidyverse dplyr commands:
mutate- generating new columns based on calculated valuesselect- selecting a subset of columns that should be included in a returned tibblefilter- selecting a subset of rows that meet specified selection criteria
Extract some key columns from the ACS dataset
The columns that we are interested in for this example are all of the populations for each age category. We are also extracting the full indentifier column (id), a subset of the identifier column that represents the combined FIPS code for the state and county (five characters, st_county), and a descriptive name of the geography (area_name). In the process of extracting the columns the long default names are replaced with more managable ones.
acs5_working <- acs5_raw %>%
mutate(
st_county = str_sub(id, -5)
) %>%
select(
id = id,
st_county,
area_name = `Geographic Area Name`,
pop_total = `Estimate!!Total!!Total population`,
pop_lt5 = `Estimate!!Total!!Total population!!AGE!!Under 5 years`,
pop_5_9 = `Estimate!!Total!!Total population!!AGE!!5 to 9 years`,
pop_10_14 = `Estimate!!Total!!Total population!!AGE!!10 to 14 years`,
pop_15_19 = `Estimate!!Total!!Total population!!AGE!!15 to 19 years`,
pop_20_24 = `Estimate!!Total!!Total population!!AGE!!20 to 24 years`,
pop_25_29 = `Estimate!!Total!!Total population!!AGE!!25 to 29 years`,
pop_30_34 = `Estimate!!Total!!Total population!!AGE!!30 to 34 years`,
pop_35_39 = `Estimate!!Total!!Total population!!AGE!!35 to 39 years`,
pop_40_44 = `Estimate!!Total!!Total population!!AGE!!40 to 44 years`,
pop_45_49 = `Estimate!!Total!!Total population!!AGE!!45 to 49 years`,
pop_50_54 = `Estimate!!Total!!Total population!!AGE!!50 to 54 years`,
pop_55_59 = `Estimate!!Total!!Total population!!AGE!!55 to 59 years`,
pop_60_64 = `Estimate!!Total!!Total population!!AGE!!60 to 64 years`,
pop_65_69 = `Estimate!!Total!!Total population!!AGE!!65 to 69 years`,
pop_70_74 = `Estimate!!Total!!Total population!!AGE!!70 to 74 years`,
pop_75_79 = `Estimate!!Total!!Total population!!AGE!!75 to 79 years`,
pop_80_84 = `Estimate!!Total!!Total population!!AGE!!80 to 84 years`,
pop_gt84 = `Estimate!!Total!!Total population!!AGE!!85 years and over`
) %>%
mutate(
pop_lt20 = pop_lt5 + pop_5_9 + pop_10_14 + pop_15_19,
pop_gte65 = pop_65_69 + pop_70_74 + pop_75_79 + pop_80_84 + pop_gt84,
pct_lt20 = pop_lt20/pop_total,
pct_gte65 = pop_gte65/pop_total
)
glimpse(acs5_working)## Observations: 3,220
## Variables: 26
## $ id <chr> "0500000US01001", "0500000US01003", "0500000US01005", "0500…
## $ st_county <chr> "01001", "01003", "01005", "01007", "01009", "01011", "0101…
## $ area_name <chr> "Autauga County, Alabama", "Baldwin County, Alabama", "Barb…
## $ pop_total <dbl> 55200, 208107, 25782, 22527, 57645, 10352, 20025, 115098, 3…
## $ pop_lt5 <dbl> 3263, 11609, 1390, 1275, 3485, 596, 1205, 6562, 1950, 1204,…
## $ pop_5_9 <dbl> 4009, 11689, 1450, 1178, 3632, 634, 1293, 6844, 1728, 1470,…
## $ pop_10_14 <dbl> 3570, 14323, 1677, 1289, 3995, 540, 1274, 7158, 2099, 1520,…
## $ pop_15_19 <dbl> 3855, 12707, 1434, 1514, 3717, 772, 1292, 7773, 1960, 1603,…
## $ pop_20_24 <dbl> 3337, 10790, 1658, 1491, 3189, 706, 1073, 7626, 2525, 1383,…
## $ pop_25_29 <dbl> 3660, 11825, 1863, 1557, 3400, 702, 1238, 8017, 1829, 1437,…
## $ pop_30_34 <dbl> 3404, 11501, 1812, 1518, 3386, 418, 1186, 7000, 1862, 1092,…
## $ pop_35_39 <dbl> 4095, 12428, 1656, 1455, 3040, 862, 1213, 7039, 1861, 1561,…
## $ pop_40_44 <dbl> 3279, 12949, 1448, 1440, 4113, 737, 1188, 6806, 2112, 1356,…
## $ pop_45_49 <dbl> 3874, 13694, 1672, 1813, 3959, 611, 1168, 7148, 2281, 1749,…
## $ pop_50_54 <dbl> 3979, 14636, 1780, 1626, 3988, 711, 1259, 7810, 2367, 1893,…
## $ pop_55_59 <dbl> 4131, 14440, 1657, 1494, 3895, 593, 1460, 7851, 2638, 2047,…
## $ pop_60_64 <dbl> 2694, 14851, 1651, 1216, 3613, 854, 1370, 8078, 2205, 1917,…
## $ pop_65_69 <dbl> 2271, 13141, 1515, 1280, 3330, 575, 1087, 6667, 2089, 1934,…
## $ pop_70_74 <dbl> 2440, 11410, 1305, 842, 2802, 436, 994, 4822, 1686, 1492, 2…
## $ pop_75_79 <dbl> 1498, 7373, 841, 624, 1776, 248, 753, 3524, 1185, 1188, 138…
## $ pop_80_84 <dbl> 1026, 4792, 551, 488, 1459, 182, 415, 2323, 747, 528, 804, …
## $ pop_gt84 <dbl> 815, 3949, 422, 427, 866, 175, 557, 2050, 702, 479, 602, 29…
## $ pop_lt20 <dbl> 14697, 50328, 5951, 5256, 14829, 2542, 5064, 28337, 7737, 5…
## $ pop_gte65 <dbl> 8050, 40665, 4634, 3661, 10233, 1616, 3806, 19386, 6409, 56…
## $ pct_lt20 <dbl> 0.2662, 0.2418, 0.2308, 0.2333, 0.2572, 0.2456, 0.2529, 0.2…
## $ pct_gte65 <dbl> 0.1458, 0.1954, 0.1797, 0.1625, 0.1775, 0.1561, 0.1901, 0.1…Extract key columns from LAD dataset
The coluns to be extracted from this dataset include the FIPS code for the state and county (STCOU in the original dataset, renamed to st_county), the descriptive area name (Areaname in the original dataset, renamed to area_name), and land area in sq. miles from the 2010 census data set (LND110210D in the original dataset, renamce to land_area_sqmi).
lad_working <- lad_raw %>%
select(
st_county = STCOU,
area_name = Areaname,
land_area_sqmi = LND110210D
)
glimpse(lad_working)## Observations: 3,198
## Variables: 3
## $ st_county <chr> "00000", "01000", "01001", "01003", "01005", "01007", …
## $ area_name <chr> "UNITED STATES", "ALABAMA", "Autauga, AL", "Baldwin, A…
## $ land_area_sqmi <dbl> 3531905.4, 50645.3, 594.4, 1589.8, 884.9, 622.6, 644.8…Build a reference table of state FIPS codes for later use in analysis and visualization
state_fips <- lad_raw %>%
filter(str_sub(STCOU, -3) == "000") %>%
mutate(
st_fips = str_sub(STCOU, 1, 2)
) %>%
select(
st_fips,
Areaname
)
glimpse(state_fips)## Observations: 52
## Variables: 2
## $ st_fips <chr> "00", "01", "02", "04", "05", "06", "08", "09", "10", "11", …
## $ Areaname <chr> "UNITED STATES", "ALABAMA", "ALASKA", "ARIZONA", "ARKANSAS",…Extract and reshape the C19 dataset
First generate the geography identifier that matches the other datasets from the UID field - st_county. Then extract the descriptive name for the geography Combined_Key, and thre remaining date columns for which there are associated confirmed infection counts.
c19_working_wide <- c19_raw %>%
mutate(
st_county = str_sub(UID, -5)
) %>%
select(
-c(
iso2,
iso3,
code3,
FIPS,
Admin2,
Province_State,
Country_Region,
Lat,
Long_
),
st_county
)
glimpse(c19_working_wide)## Observations: 3,340
## Variables: 402
## $ UID <dbl> 84001001, 84001003, 84001005, 84001007, 84001009, 840010…
## $ Combined_Key <chr> "Autauga, Alabama, US", "Baldwin, Alabama, US", "Barbour…
## $ `1/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/15/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/16/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/17/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/18/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/19/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/20/20` <dbl> 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/21/20` <dbl> 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/22/20` <dbl> 0, 2, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/23/20` <dbl> 0, 3, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/24/20` <dbl> 1, 5, 0, 0, 0, 0, 0, 2, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/25/20` <dbl> 5, 5, 0, 0, 3, 0, 1, 2, 9, 1, 4, 0, 0, 1, 1, 0, 1, 0, 1,…
## $ `3/26/20` <dbl> 6, 6, 0, 0, 4, 1, 1, 2, 13, 1, 4, 1, 0, 1, 1, 0, 1, 0, 2…
## $ `3/27/20` <dbl> 6, 6, 0, 2, 6, 1, 1, 4, 15, 1, 6, 1, 0, 1, 3, 0, 1, 0, 2…
## $ `3/28/20` <dbl> 6, 18, 0, 2, 6, 2, 1, 4, 22, 1, 7, 1, 0, 2, 4, 0, 2, 0, …
## $ `3/29/20` <dbl> 6, 19, 0, 2, 7, 2, 1, 4, 29, 1, 8, 1, 0, 2, 5, 0, 2, 0, …
## $ `3/30/20` <dbl> 8, 22, 0, 3, 7, 2, 1, 9, 39, 1, 10, 2, 0, 2, 5, 0, 4, 0,…
## $ `3/31/20` <dbl> 8, 23, 0, 3, 7, 2, 1, 10, 40, 1, 11, 4, 0, 2, 5, 0, 4, 0…
## $ `4/1/20` <dbl> 10, 26, 0, 3, 7, 2, 1, 11, 51, 1, 13, 5, 2, 3, 6, 1, 4, …
## $ `4/2/20` <dbl> 12, 29, 0, 4, 9, 2, 1, 12, 75, 3, 14, 5, 2, 7, 6, 4, 5, …
## $ `4/3/20` <dbl> 12, 31, 2, 4, 11, 2, 1, 22, 87, 4, 15, 5, 3, 8, 7, 7, 5,…
## $ `4/4/20` <dbl> 12, 34, 3, 4, 12, 2, 1, 23, 91, 5, 15, 5, 7, 9, 7, 8, 6,…
## $ `4/5/20` <dbl> 12, 39, 3, 7, 13, 2, 1, 26, 94, 5, 18, 6, 9, 9, 7, 8, 7,…
## $ `4/6/20` <dbl> 12, 43, 4, 7, 13, 2, 1, 40, 102, 5, 20, 8, 9, 9, 9, 9, 8…
## $ `4/7/20` <dbl> 12, 47, 4, 8, 13, 2, 2, 51, 112, 5, 21, 8, 10, 9, 12, 9,…
## $ `4/8/20` <dbl> 12, 54, 4, 9, 13, 3, 3, 54, 145, 7, 23, 8, 10, 11, 12, 1…
## $ `4/9/20` <dbl> 17, 63, 8, 11, 15, 4, 3, 56, 170, 7, 26, 8, 13, 11, 12, …
## $ `4/10/20` <dbl> 18, 64, 9, 11, 15, 4, 3, 56, 178, 7, 29, 10, 13, 11, 12,…
## $ `4/11/20` <dbl> 19, 70, 10, 13, 15, 4, 6, 59, 189, 7, 31, 11, 15, 12, 12…
## $ `4/12/20` <dbl> 19, 75, 10, 16, 17, 4, 7, 63, 206, 9, 31, 12, 18, 14, 12…
## $ `4/13/20` <dbl> 19, 82, 10, 17, 18, 6, 8, 64, 215, 9, 35, 12, 18, 14, 12…
## $ `4/14/20` <dbl> 23, 93, 11, 18, 19, 8, 8, 65, 219, 9, 35, 14, 18, 14, 12…
## $ `4/15/20` <dbl> 24, 100, 13, 19, 21, 8, 11, 66, 229, 10, 38, 15, 19, 14,…
## $ `4/16/20` <dbl> 24, 104, 14, 23, 22, 8, 11, 68, 234, 11, 38, 15, 21, 14,…
## $ `4/17/20` <dbl> 24, 106, 15, 23, 22, 8, 13, 68, 239, 11, 39, 15, 21, 14,…
## $ `4/18/20` <dbl> 25, 110, 18, 26, 24, 9, 14, 71, 242, 12, 41, 16, 21, 16,…
## $ `4/19/20` <dbl> 26, 116, 20, 26, 25, 9, 15, 78, 247, 12, 45, 16, 21, 17,…
## $ `4/20/20` <dbl> 28, 120, 22, 30, 27, 11, 15, 82, 257, 13, 45, 18, 22, 18…
## $ `4/21/20` <dbl> 30, 126, 28, 30, 31, 11, 15, 85, 259, 14, 45, 18, 22, 18…
## $ `4/22/20` <dbl> 32, 134, 29, 32, 33, 11, 17, 87, 266, 14, 47, 21, 22, 18…
## $ `4/23/20` <dbl> 33, 143, 31, 32, 35, 12, 19, 89, 273, 14, 49, 23, 23, 18…
## $ `4/24/20` <dbl> 36, 148, 33, 33, 36, 12, 21, 89, 277, 14, 51, 23, 23, 18…
## $ `4/25/20` <dbl> 36, 155, 34, 34, 36, 12, 28, 90, 279, 14, 52, 25, 24, 18…
## $ `4/26/20` <dbl> 36, 161, 34, 37, 38, 12, 32, 91, 281, 15, 53, 30, 25, 18…
## $ `4/27/20` <dbl> 37, 167, 35, 40, 38, 12, 34, 91, 284, 15, 53, 36, 26, 18…
## $ `4/28/20` <dbl> 39, 172, 38, 40, 38, 12, 48, 94, 286, 16, 54, 37, 27, 18…
## $ `4/29/20` <dbl> 41, 174, 38, 40, 39, 12, 53, 95, 288, 16, 54, 37, 30, 20…
## $ `4/30/20` <dbl> 42, 180, 38, 40, 39, 13, 55, 97, 292, 16, 55, 44, 31, 20…
## $ `5/1/20` <dbl> 43, 180, 42, 41, 41, 14, 68, 97, 295, 16, 55, 50, 33, 20…
## $ `5/2/20` <dbl> 47, 185, 43, 41, 42, 14, 93, 102, 296, 16, 56, 50, 37, 2…
## $ `5/3/20` <dbl> 51, 190, 45, 42, 42, 14, 104, 109, 302, 19, 59, 50, 41, …
## $ `5/4/20` <dbl> 54, 190, 45, 43, 42, 15, 113, 109, 307, 19, 61, 54, 41, …
## $ `5/5/20` <dbl> 54, 194, 47, 43, 42, 17, 118, 118, 309, 19, 64, 56, 42, …
## $ `5/6/20` <dbl> 56, 199, 47, 43, 44, 17, 128, 120, 313, 20, 64, 56, 44, …
## $ `5/7/20` <dbl> 58, 207, 50, 44, 46, 17, 152, 126, 317, 20, 69, 57, 48, …
## $ `5/8/20` <dbl> 62, 211, 52, 44, 47, 20, 159, 129, 320, 22, 69, 60, 51, …
## $ `5/9/20` <dbl> 63, 219, 57, 45, 47, 21, 175, 130, 323, 23, 70, 62, 51, …
## $ `5/10/20` <dbl> 72, 224, 59, 46, 47, 22, 188, 130, 325, 23, 72, 67, 55, …
## $ `5/11/20` <dbl> 81, 226, 61, 46, 48, 26, 196, 131, 327, 24, 74, 68, 62, …
## $ `5/12/20` <dbl> 88, 230, 67, 46, 48, 26, 223, 132, 332, 24, 76, 70, 64, …
## $ `5/13/20` <dbl> 90, 237, 70, 46, 48, 27, 231, 133, 333, 24, 76, 73, 65, …
## $ `5/14/20` <dbl> 100, 247, 74, 47, 48, 27, 244, 134, 334, 24, 78, 77, 67,…
## $ `5/15/20` <dbl> 100, 253, 79, 51, 48, 32, 254, 135, 335, 25, 79, 81, 70,…
## $ `5/16/20` <dbl> 108, 261, 81, 52, 49, 35, 265, 136, 337, 26, 81, 84, 74,…
## $ `5/17/20` <dbl> 118, 267, 85, 52, 49, 39, 278, 139, 337, 27, 83, 85, 75,…
## $ `5/18/20` <dbl> 124, 267, 89, 53, 50, 50, 289, 139, 338, 28, 84, 89, 77,…
## $ `5/19/20` <dbl> 130, 269, 92, 53, 50, 58, 297, 142, 339, 30, 85, 124, 86…
## $ `5/20/20` <dbl> 135, 277, 96, 54, 50, 64, 306, 142, 342, 30, 87, 127, 89…
## $ `5/21/20` <dbl> 148, 277, 101, 54, 51, 71, 315, 143, 343, 31, 88, 132, 9…
## $ `5/22/20` <dbl> 151, 278, 106, 56, 52, 95, 325, 145, 343, 32, 89, 135, 9…
## $ `5/23/20` <dbl> 156, 280, 108, 59, 52, 103, 329, 145, 343, 32, 90, 139, …
## $ `5/24/20` <dbl> 160, 281, 113, 60, 52, 109, 338, 148, 349, 32, 91, 140, …
## $ `5/25/20` <dbl> 171, 284, 116, 63, 52, 138, 360, 153, 350, 33, 91, 143, …
## $ `5/26/20` <dbl> 191, 285, 128, 68, 54, 167, 375, 157, 353, 33, 95, 146, …
## $ `5/27/20` <dbl> 192, 289, 133, 72, 56, 175, 387, 159, 356, 33, 99, 146, …
## $ `5/28/20` <dbl> 204, 289, 136, 72, 61, 185, 389, 159, 364, 34, 102, 147,…
## $ `5/29/20` <dbl> 211, 290, 151, 72, 63, 201, 394, 160, 367, 36, 104, 148,…
## $ `5/30/20` <dbl> 216, 292, 155, 74, 65, 205, 403, 161, 369, 37, 104, 150,…
## $ `5/31/20` <dbl> 227, 298, 172, 77, 66, 209, 413, 169, 370, 38, 105, 152,…
## $ `6/1/20` <dbl> 237, 299, 177, 78, 66, 211, 414, 172, 373, 38, 106, 152,…
## $ `6/2/20` <dbl> 239, 301, 181, 78, 66, 215, 418, 172, 374, 38, 108, 152,…
## $ `6/3/20` <dbl> 241, 302, 181, 78, 66, 217, 420, 174, 375, 38, 111, 152,…
## $ `6/4/20` <dbl> 248, 305, 187, 78, 67, 219, 430, 176, 379, 38, 112, 152,…
## $ `6/5/20` <dbl> 259, 312, 194, 79, 73, 225, 441, 181, 389, 40, 112, 154,…
## $ `6/6/20` <dbl> 264, 321, 196, 79, 76, 231, 447, 183, 393, 42, 115, 154,…
## $ `6/7/20` <dbl> 271, 327, 200, 81, 78, 237, 453, 184, 398, 42, 115, 155,…
## $ `6/8/20` <dbl> 282, 332, 201, 87, 80, 242, 462, 186, 407, 42, 121, 156,…
## $ `6/9/20` <dbl> 295, 338, 210, 91, 84, 247, 469, 188, 418, 43, 124, 157,…
## $ `6/10/20` <dbl> 315, 350, 215, 95, 92, 252, 482, 190, 434, 43, 127, 161,…
## $ `6/11/20` <dbl> 323, 357, 220, 97, 98, 256, 498, 193, 443, 46, 128, 163,…
## $ `6/12/20` <dbl> 334, 365, 226, 102, 106, 275, 517, 197, 457, 48, 134, 16…
## $ `6/13/20` <dbl> 361, 367, 233, 106, 114, 300, 534, 201, 474, 52, 138, 17…
## $ `6/14/20` <dbl> 369, 375, 237, 113, 120, 305, 542, 202, 486, 52, 143, 17…
## $ `6/15/20` <dbl> 371, 377, 243, 113, 121, 306, 545, 203, 488, 52, 143, 17…
## $ `6/16/20` <dbl> 377, 379, 252, 116, 125, 310, 548, 206, 491, 53, 151, 17…
## $ `6/17/20` <dbl> 404, 389, 264, 119, 135, 316, 560, 208, 503, 54, 154, 18…
## $ `6/18/20` <dbl> 415, 402, 267, 125, 140, 317, 562, 208, 507, 57, 157, 18…
## $ `6/19/20` <dbl> 435, 408, 272, 125, 144, 324, 563, 209, 515, 57, 162, 18…
## $ `6/20/20` <dbl> 438, 418, 272, 126, 151, 324, 567, 211, 520, 57, 165, 18…
## $ `6/21/20` <dbl> 447, 426, 277, 128, 155, 325, 569, 213, 527, 57, 170, 18…
## $ `6/22/20` <dbl> 458, 439, 280, 134, 162, 327, 572, 214, 533, 57, 176, 18…
## $ `6/23/20` <dbl> 474, 453, 288, 141, 170, 330, 574, 223, 540, 58, 184, 18…
## $ `6/24/20` <dbl> 480, 466, 305, 148, 177, 339, 580, 233, 547, 62, 184, 18…
## $ `6/25/20` <dbl> 493, 505, 313, 161, 186, 340, 586, 236, 558, 66, 192, 18…
## $ `6/26/20` <dbl> 500, 549, 317, 164, 192, 346, 593, 245, 560, 67, 199, 18…
## $ `6/27/20` <dbl> 505, 573, 317, 164, 192, 346, 595, 245, 560, 69, 200, 18…
## $ `6/28/20` <dbl> 528, 628, 324, 167, 200, 354, 598, 261, 581, 72, 208, 19…
## $ `6/29/20` <dbl> 538, 668, 327, 172, 209, 354, 599, 276, 590, 73, 211, 19…
## $ `6/30/20` <dbl> 554, 691, 328, 176, 218, 356, 599, 278, 594, 76, 218, 19…
## $ `7/1/20` <dbl> 562, 739, 337, 181, 221, 358, 602, 288, 609, 82, 221, 19…
## $ `7/2/20` <dbl> 570, 831, 352, 191, 232, 363, 614, 330, 620, 89, 234, 19…
## $ `7/3/20` <dbl> 584, 847, 354, 193, 236, 363, 615, 341, 631, 89, 244, 19…
## $ `7/4/20` <dbl> 601, 861, 356, 196, 240, 363, 624, 363, 634, 101, 251, 1…
## $ `7/5/20` <dbl> 620, 893, 358, 198, 249, 364, 624, 375, 643, 102, 258, 1…
## $ `7/6/20` <dbl> 623, 979, 365, 202, 257, 365, 625, 390, 652, 106, 264, 1…
## $ `7/7/20` <dbl> 656, 1042, 372, 204, 270, 366, 630, 412, 658, 115, 281, …
## $ `7/8/20` <dbl> 663, 1118, 377, 214, 289, 366, 632, 443, 672, 115, 292, …
## $ `7/9/20` <dbl> 670, 1175, 387, 219, 301, 372, 639, 460, 678, 116, 300, …
## $ `7/10/20` <dbl> 691, 1215, 404, 226, 316, 373, 645, 496, 690, 126, 329, …
## $ `7/11/20` <dbl> 708, 1280, 409, 229, 338, 373, 646, 520, 694, 128, 337, …
## $ `7/12/20` <dbl> 736, 1349, 419, 232, 354, 374, 650, 562, 703, 135, 353, …
## $ `7/13/20` <dbl> 749, 1396, 437, 237, 368, 376, 651, 580, 713, 139, 364, …
## $ `7/14/20` <dbl> 764, 1506, 446, 243, 390, 377, 655, 649, 719, 144, 377, …
## $ `7/15/20` <dbl> 785, 1588, 462, 248, 426, 380, 659, 663, 732, 149, 394, …
## $ `7/16/20` <dbl> 797, 1675, 466, 255, 445, 383, 662, 706, 743, 155, 413, …
## $ `7/17/20` <dbl> 822, 1806, 483, 262, 464, 389, 668, 728, 756, 160, 431, …
## $ `7/18/20` <dbl> 850, 1925, 497, 270, 487, 390, 676, 772, 762, 171, 469, …
## $ `7/19/20` <dbl> 862, 1996, 504, 277, 510, 392, 678, 813, 767, 177, 482, …
## $ `7/20/20` <dbl> 872, 2085, 513, 283, 526, 393, 685, 849, 774, 181, 498, …
## $ `7/21/20` <dbl> 885, 2169, 520, 288, 549, 396, 693, 870, 781, 183, 516, …
## $ `7/22/20` <dbl> 905, 2344, 531, 290, 582, 401, 699, 893, 789, 192, 538, …
## $ `7/23/20` <dbl> 918, 2482, 540, 304, 616, 405, 705, 974, 796, 205, 563, …
## $ `7/24/20` <dbl> 939, 2610, 552, 318, 639, 410, 712, 1025, 810, 207, 588,…
## $ `7/25/20` <dbl> 953, 2675, 563, 324, 654, 411, 718, 1086, 820, 209, 603,…
## $ `7/26/20` <dbl> 971, 2733, 570, 335, 674, 412, 724, 1163, 824, 220, 622,…
## $ `7/27/20` <dbl> 988, 2800, 576, 339, 679, 425, 728, 1206, 835, 221, 640,…
## $ `7/28/20` <dbl> 995, 2873, 585, 345, 701, 427, 734, 1260, 844, 227, 664,…
## $ `7/29/20` <dbl> 1006, 3001, 586, 353, 738, 431, 740, 1305, 848, 235, 677…
## $ `7/30/20` <dbl> 1029, 3075, 598, 364, 773, 433, 746, 1412, 859, 238, 693…
## $ `7/31/20` <dbl> 1042, 3116, 603, 369, 799, 439, 749, 1445, 862, 242, 709…
## $ `8/1/20` <dbl> 1064, 3194, 611, 373, 820, 441, 756, 1541, 869, 254, 731…
## $ `8/2/20` <dbl> 1078, 3241, 613, 383, 838, 442, 758, 1570, 876, 262, 743…
## $ `8/3/20` <dbl> 1086, 3298, 615, 390, 841, 445, 758, 1608, 882, 268, 759…
## $ `8/4/20` <dbl> 1086, 3347, 616, 393, 845, 448, 761, 1645, 887, 270, 775…
## $ `8/5/20` <dbl> 1109, 3409, 621, 420, 883, 454, 763, 1704, 894, 281, 784…
## $ `8/6/20` <dbl> 1126, 3473, 627, 424, 917, 458, 766, 1744, 900, 291, 809…
## $ `8/7/20` <dbl> 1145, 3533, 629, 434, 929, 467, 766, 1795, 905, 298, 829…
## $ `8/8/20` <dbl> 1175, 3575, 632, 446, 945, 468, 771, 1821, 906, 301, 834…
## $ `8/9/20` <dbl> 1186, 3676, 633, 450, 957, 470, 774, 1847, 909, 302, 852…
## $ `8/10/20` <dbl> 1200, 3700, 642, 454, 971, 486, 775, 1874, 915, 306, 862…
## $ `8/11/20` <dbl> 1224, 3749, 648, 465, 979, 497, 778, 1902, 918, 311, 873…
## $ `8/12/20` <dbl> 1229, 3783, 653, 469, 994, 497, 780, 1925, 919, 319, 877…
## $ `8/13/20` <dbl> 1235, 3825, 658, 475, 998, 498, 786, 1941, 922, 326, 890…
## $ `8/14/20` <dbl> 1245, 3881, 662, 479, 1004, 500, 796, 1980, 925, 333, 89…
## $ `8/15/20` <dbl> 1252, 3923, 672, 481, 1014, 500, 799, 2000, 927, 337, 90…
## $ `8/16/20` <dbl> 1258, 3936, 673, 485, 1023, 501, 803, 2015, 928, 341, 91…
## $ `8/17/20` <dbl> 1276, 3959, 675, 487, 1060, 512, 803, 2061, 937, 350, 92…
## $ `8/18/20` <dbl> 1281, 3994, 682, 501, 1086, 530, 805, 2121, 943, 351, 93…
## $ `8/19/20` <dbl> 1293, 4029, 692, 507, 1113, 535, 809, 2181, 949, 358, 94…
## $ `8/20/20` <dbl> 1304, 4058, 694, 512, 1133, 536, 811, 2211, 965, 362, 96…
## $ `8/21/20` <dbl> 1316, 4100, 710, 515, 1147, 539, 813, 2246, 969, 367, 98…
## $ `8/22/20` <dbl> 1318, 4132, 712, 521, 1149, 539, 815, 2260, 970, 369, 99…
## $ `8/23/20` <dbl> 1337, 4148, 715, 521, 1160, 540, 816, 2285, 970, 374, 10…
## $ `8/24/20` <dbl> 1343, 4171, 720, 522, 1177, 540, 817, 2302, 973, 376, 10…
## $ `8/25/20` <dbl> 1357, 4247, 728, 526, 1224, 541, 824, 2360, 982, 379, 10…
## $ `8/26/20` <dbl> 1365, 4296, 736, 530, 1239, 542, 827, 2380, 1012, 387, 1…
## $ `8/27/20` <dbl> 1375, 4330, 741, 532, 1251, 544, 829, 2409, 1015, 390, 1…
## $ `8/28/20` <dbl> 1391, 4408, 749, 539, 1273, 551, 845, 2468, 1023, 395, 1…
## $ `8/29/20` <dbl> 1424, 4502, 752, 550, 1301, 555, 855, 2500, 1024, 402, 1…
## $ `8/30/20` <dbl> 1429, 4519, 752, 553, 1312, 556, 857, 2530, 1029, 403, 1…
## $ `8/31/20` <dbl> 1440, 4538, 759, 558, 1332, 564, 865, 2582, 1037, 414, 1…
## $ `9/1/20` <dbl> 1442, 4563, 763, 562, 1336, 566, 867, 2606, 1040, 419, 1…
## $ `9/2/20` <dbl> 1454, 4599, 765, 564, 1356, 567, 868, 2648, 1041, 426, 1…
## $ `9/3/20` <dbl> 1462, 4626, 770, 567, 1376, 567, 872, 2714, 1047, 440, 1…
## $ `9/4/20` <dbl> 1474, 4654, 770, 571, 1392, 567, 875, 2786, 1053, 447, 1…
## $ `9/5/20` <dbl> 1477, 4684, 771, 576, 1399, 569, 879, 2797, 1055, 449, 1…
## $ `9/6/20` <dbl> 1488, 4700, 772, 578, 1416, 569, 881, 2836, 1058, 452, 1…
## $ `9/7/20` <dbl> 1494, 4725, 772, 585, 1418, 569, 881, 2840, 1058, 452, 1…
## $ `9/8/20` <dbl> 1505, 4755, 772, 586, 1424, 569, 882, 2859, 1061, 455, 1…
## $ `9/9/20` <dbl> 1526, 4796, 779, 589, 1441, 571, 883, 2900, 1069, 465, 1…
## $ `9/10/20` <dbl> 1530, 4845, 781, 593, 1453, 571, 884, 2922, 1079, 471, 1…
## $ `9/11/20` <dbl> 1543, 4881, 787, 594, 1459, 572, 886, 2975, 1085, 482, 1…
## $ `9/12/20` <dbl> 1551, 4915, 789, 596, 1472, 578, 888, 3023, 1085, 502, 1…
## $ `9/13/20` <dbl> 1567, 4934, 796, 599, 1483, 578, 889, 3040, 1087, 504, 1…
## $ `9/14/20` <dbl> 1586, 4949, 801, 600, 1490, 579, 890, 3066, 1092, 506, 1…
## $ `9/15/20` <dbl> 1601, 4964, 807, 601, 1504, 581, 892, 3106, 1097, 510, 1…
## $ `9/16/20` <dbl> 1614, 4982, 807, 606, 1515, 583, 893, 3156, 1097, 513, 1…
## $ `9/17/20` <dbl> 1650, 4994, 822, 607, 1538, 583, 896, 3189, 1109, 522, 1…
## $ `9/18/20` <dbl> 1659, 5016, 825, 619, 1551, 584, 897, 3268, 1114, 539, 1…
## $ `9/19/20` <dbl> 1675, 5029, 831, 623, 1564, 586, 897, 3289, 1121, 549, 1…
## $ `9/20/20` <dbl> 1676, 5053, 833, 624, 1573, 590, 898, 3321, 1123, 552, 1…
## $ `9/21/20` <dbl> 1697, 5090, 846, 628, 1586, 592, 898, 3341, 1131, 557, 1…
## $ `9/22/20` <dbl> 1697, 5106, 848, 633, 1593, 596, 899, 3353, 1135, 567, 1…
## $ `9/23/20` <dbl> 1711, 5127, 852, 637, 1605, 597, 901, 3412, 1142, 574, 1…
## $ `9/24/20` <dbl> 1736, 5397, 868, 646, 1614, 598, 903, 3450, 1151, 586, 1…
## $ `9/25/20` <dbl> 1750, 5419, 878, 649, 1619, 603, 906, 3466, 1154, 592, 1…
## $ `9/26/20` <dbl> 1758, 5465, 882, 650, 1623, 605, 907, 3485, 1157, 596, 1…
## $ `9/27/20` <dbl> 1770, 5524, 883, 651, 1624, 606, 908, 3498, 1161, 597, 1…
## $ `9/28/20` <dbl> 1776, 5550, 883, 653, 1626, 606, 911, 3516, 1164, 601, 1…
## $ `9/29/20` <dbl> 1785, 5592, 892, 660, 1633, 608, 913, 3530, 1170, 605, 1…
## $ `9/30/20` <dbl> 1792, 5954, 894, 668, 1636, 611, 915, 3556, 1172, 609, 1…
## $ `10/1/20` <dbl> 1799, 5981, 900, 671, 1644, 611, 917, 3597, 1175, 616, 1…
## $ `10/2/20` <dbl> 1812, 6009, 917, 675, 1656, 611, 919, 3629, 1195, 625, 1…
## $ `10/3/20` <dbl> 1821, 6034, 917, 683, 1659, 611, 920, 3647, 1196, 625, 1…
## $ `10/4/20` <dbl> 1824, 6045, 917, 683, 1664, 611, 921, 3672, 1199, 628, 1…
## $ `10/5/20` <dbl> 1832, 6075, 918, 688, 1667, 614, 923, 3700, 1201, 630, 1…
## $ `10/6/20` <dbl> 1843, 6103, 923, 700, 1678, 616, 926, 3718, 1203, 636, 1…
## $ `10/7/20` <dbl> 1847, 6114, 925, 703, 1686, 618, 928, 3736, 1212, 640, 1…
## $ `10/8/20` <dbl> 1875, 6144, 937, 715, 1700, 622, 937, 3790, 1231, 648, 1…
## $ `10/9/20` <dbl> 1894, 6164, 940, 724, 1712, 623, 948, 3811, 1235, 653, 1…
## $ `10/10/20` <dbl> 1901, 6176, 940, 734, 1720, 624, 954, 3829, 1235, 657, 1…
## $ `10/11/20` <dbl> 1907, 6192, 941, 736, 1732, 625, 958, 3848, 1238, 661, 1…
## $ `10/12/20` <dbl> 1921, 6222, 948, 741, 1755, 626, 964, 3884, 1247, 674, 1…
## $ `10/13/20` <dbl> 1925, 6247, 948, 742, 1765, 628, 974, 3911, 1251, 677, 1…
## $ `10/14/20` <dbl> 1946, 6266, 963, 759, 1782, 628, 979, 3939, 1255, 687, 1…
## $ `10/15/20` <dbl> 1958, 6313, 966, 769, 1796, 630, 985, 3992, 1258, 694, 1…
## $ `10/16/20` <dbl> 1971, 6332, 975, 773, 1822, 633, 987, 4053, 1261, 705, 1…
## $ `10/17/20` <dbl> 1985, 6350, 978, 783, 1837, 633, 993, 4071, 1261, 706, 1…
## $ `10/18/20` <dbl> 1995, 6356, 978, 787, 1848, 634, 996, 4090, 1268, 707, 1…
## $ `10/19/20` <dbl> 2006, 6384, 984, 789, 1863, 635, 996, 4115, 1298, 714, 1…
## $ `10/20/20` <dbl> 2018, 6425, 993, 799, 1887, 636, 996, 4143, 1328, 717, 1…
## $ `10/21/20` <dbl> 2021, 6459, 1007, 809, 1907, 636, 999, 4175, 1334, 720, …
## $ `10/22/20` <dbl> 2027, 6599, 1010, 823, 1923, 638, 1000, 4213, 1341, 725,…
## $ `10/23/20` <dbl> 2040, 6619, 1028, 825, 1934, 647, 1005, 4553, 1346, 727,…
## $ `10/24/20` <dbl> 2055, 6642, 1030, 839, 1947, 648, 1009, 4587, 1347, 732,…
## $ `10/25/20` <dbl> 2070, 6677, 1030, 841, 1958, 648, 1011, 4602, 1349, 736,…
## $ `10/26/20` <dbl> 2079, 6694, 1038, 849, 1986, 649, 1011, 4634, 1365, 749,…
## $ `10/27/20` <dbl> 2098, 6728, 1042, 858, 2002, 649, 1014, 4675, 1369, 756,…
## $ `10/28/20` <dbl> 2120, 6757, 1052, 862, 2027, 650, 1018, 4757, 1379, 759,…
## $ `10/29/20` <dbl> 2134, 6879, 1053, 867, 2054, 651, 1018, 4800, 1381, 764,…
## $ `10/30/20` <dbl> 2154, 6931, 1058, 873, 2089, 651, 1021, 4855, 1389, 779,…
## $ `10/31/20` <dbl> 2168, 6955, 1059, 877, 2109, 653, 1023, 4883, 1392, 782,…
## $ `11/1/20` <dbl> 2182, 6974, 1062, 883, 2128, 653, 1025, 4913, 1397, 786,…
## $ `11/2/20` <dbl> 2195, 6991, 1073, 890, 2178, 656, 1028, 4946, 1428, 804,…
## $ `11/3/20` <dbl> 2210, 7054, 1077, 900, 2204, 658, 1032, 4996, 1449, 819,…
## $ `11/4/20` <dbl> 2229, 7093, 1079, 907, 2233, 660, 1035, 5030, 1461, 830,…
## $ `11/5/20` <dbl> 2244, 7133, 1089, 920, 2258, 662, 1043, 5072, 1469, 833,…
## $ `11/6/20` <dbl> 2257, 7184, 1092, 926, 2290, 663, 1045, 5146, 1483, 837,…
## $ `11/7/20` <dbl> 2286, 7226, 1095, 934, 2302, 664, 1051, 5179, 1485, 841,…
## $ `11/8/20` <dbl> 2307, 7263, 1098, 942, 2338, 664, 1053, 5214, 1488, 846,…
## $ `11/9/20` <dbl> 2328, 7345, 1107, 948, 2378, 665, 1061, 5246, 1506, 857,…
## $ `11/10/20` <dbl> 2328, 7348, 1107, 948, 2378, 665, 1061, 5254, 1507, 857,…
## $ `11/11/20` <dbl> 2351, 7409, 1112, 961, 2400, 668, 1062, 5282, 1508, 865,…
## $ `11/12/20` <dbl> 2385, 7454, 1113, 966, 2429, 669, 1062, 5345, 1514, 870,…
## $ `11/13/20` <dbl> 2417, 7523, 1117, 973, 2488, 673, 1068, 5429, 1545, 892,…
## $ `11/14/20` <dbl> 2435, 7596, 1123, 978, 2518, 675, 1075, 5470, 1556, 898,…
## $ `11/15/20` <dbl> 2456, 7646, 1128, 986, 2549, 677, 1087, 5608, 1570, 908,…
## $ `11/16/20` <dbl> 2481, 7696, 1130, 993, 2574, 677, 1095, 5666, 1572, 912,…
## $ `11/17/20` <dbl> 2506, 7772, 1134, 1004, 2594, 678, 1099, 5702, 1595, 919…
## $ `11/18/20` <dbl> 2529, 7849, 1137, 1008, 2648, 678, 1102, 5764, 1620, 935…
## $ `11/19/20` <dbl> 2554, 7933, 1145, 1011, 2683, 680, 1113, 5814, 1641, 956…
## $ `11/20/20` <dbl> 2580, 8038, 1151, 1024, 2704, 684, 1120, 5896, 1663, 959…
## $ `11/21/20` <dbl> 2597, 8131, 1157, 1036, 2735, 688, 1132, 5924, 1669, 979…
## $ `11/22/20` <dbl> 2617, 8199, 1160, 1136, 2754, 689, 1133, 5964, 1675, 985…
## $ `11/23/20` <dbl> 2634, 8269, 1161, 1142, 2763, 690, 1137, 5997, 1680, 989…
## $ `11/24/20` <dbl> 2661, 8376, 1167, 1157, 2822, 690, 1143, 6049, 1714, 996…
## $ `11/25/20` <dbl> 2686, 8473, 1170, 1162, 2855, 691, 1144, 6112, 1737, 100…
## $ `11/26/20` <dbl> 2704, 8576, 1170, 1170, 2879, 694, 1153, 6215, 1764, 101…
## $ `11/27/20` <dbl> 2716, 8603, 1171, 1173, 2888, 694, 1153, 6240, 1765, 101…
## $ `11/28/20` <dbl> 2735, 8733, 1173, 1179, 2922, 696, 1165, 6301, 1768, 102…
## $ `11/29/20` <dbl> 2751, 8820, 1175, 1188, 2946, 700, 1173, 6366, 1772, 102…
## $ `11/30/20` <dbl> 2780, 8890, 1178, 1196, 2997, 702, 1178, 6430, 1779, 103…
## $ `12/1/20` <dbl> 2818, 9051, 1189, 1204, 3061, 701, 1186, 6598, 1827, 105…
## $ `12/2/20` <dbl> 2873, 9163, 1206, 1239, 3100, 709, 1188, 6695, 1859, 105…
## $ `12/3/20` <dbl> 2893, 9341, 1214, 1252, 3158, 709, 1200, 6809, 1875, 106…
## $ `12/4/20` <dbl> 2945, 9501, 1217, 1270, 3231, 711, 1211, 6939, 1891, 108…
## $ `12/5/20` <dbl> 2979, 9626, 1219, 1283, 3281, 713, 1225, 7027, 1901, 109…
## $ `12/6/20` <dbl> 3005, 9728, 1223, 1293, 3299, 713, 1236, 7096, 1906, 109…
## $ `12/7/20` <dbl> 3043, 9821, 1224, 1299, 3324, 714, 1244, 7165, 1915, 111…
## $ `12/8/20` <dbl> 3087, 9974, 1240, 1317, 3426, 719, 1257, 7300, 1945, 112…
## $ `12/9/20` <dbl> 3117, 10087, 1245, 1322, 3496, 722, 1263, 7392, 1961, 11…
## $ `12/10/20` <dbl> 3186, 10288, 1258, 1359, 3600, 722, 1287, 7534, 1977, 11…
## $ `12/11/20` <dbl> 3233, 10489, 1264, 1398, 3663, 723, 1289, 7658, 1982, 11…
## $ `12/12/20` <dbl> 3258, 10665, 1269, 1417, 3744, 725, 1306, 7760, 1997, 11…
## $ `12/13/20` <dbl> 3300, 10806, 1272, 1441, 3776, 728, 1330, 7813, 2013, 11…
## $ `12/14/20` <dbl> 3329, 10898, 1275, 1455, 3803, 728, 1340, 7872, 2022, 12…
## $ `12/15/20` <dbl> 3426, 11061, 1292, 1504, 3881, 733, 1332, 7966, 2040, 12…
## $ `12/16/20` <dbl> 3510, 11212, 1296, 1520, 3950, 737, 1343, 8072, 2064, 12…
## $ `12/17/20` <dbl> 3570, 11364, 1309, 1548, 4036, 742, 1368, 8290, 2076, 12…
## $ `12/18/20` <dbl> 3647, 11556, 1318, 1577, 4118, 747, 1384, 8459, 2090, 12…
## $ `12/19/20` <dbl> 3698, 11722, 1330, 1601, 4191, 752, 1393, 8594, 2116, 12…
## $ `12/20/20` <dbl> 3741, 11827, 1336, 1613, 4218, 753, 1399, 8648, 2125, 12…
## $ `12/21/20` <dbl> 3780, 11952, 1336, 1628, 4234, 754, 1405, 8684, 2133, 12…
## $ `12/22/20` <dbl> 3841, 12155, 1363, 1660, 4313, 760, 1412, 8856, 2161, 13…
## $ `12/23/20` <dbl> 3889, 12321, 1383, 1683, 4367, 765, 1423, 8968, 2176, 13…
## $ `12/24/20` <dbl> 3942, 12521, 1390, 1711, 4405, 770, 1434, 9071, 2191, 13…
## $ `12/25/20` <dbl> 3990, 12666, 1396, 1725, 4441, 777, 1448, 9167, 2200, 13…
## $ `12/26/20` <dbl> 3999, 12708, 1398, 1739, 4446, 825, 1446, 9198, 2203, 13…
## $ `12/27/20` <dbl> 4029, 12825, 1406, 1746, 4465, 827, 1452, 9232, 2214, 13…
## $ `12/28/20` <dbl> 4065, 12962, 1417, 1762, 4483, 830, 1457, 9286, 2229, 13…
## $ `12/29/20` <dbl> 4105, 13172, 1462, 1792, 4535, 834, 1482, 9345, 2275, 13…
## $ `12/30/20` <dbl> 4164, 13392, 1492, 1817, 4584, 846, 1493, 9428, 2310, 14…
## $ `12/31/20` <dbl> 4190, 13601, 1514, 1834, 4641, 859, 1508, 9494, 2341, 14…
## $ `1/1/21` <dbl> 4239, 13823, 1517, 1854, 4693, 888, 1522, 9584, 2366, 14…
## $ `1/2/21` <dbl> 4268, 13955, 1528, 1863, 4729, 892, 1530, 9692, 2386, 14…
## $ `1/3/21` <dbl> 4305, 14064, 1530, 1882, 4746, 900, 1546, 9731, 2402, 14…
## $ `1/4/21` <dbl> 4336, 14187, 1533, 1885, 4771, 910, 1554, 9752, 2415, 14…
## $ `1/5/21` <dbl> 4546, 14440, 1575, 1923, 4849, 920, 1574, 9975, 2474, 14…
## $ `1/6/21` <dbl> 4645, 14656, 1597, 1944, 4898, 925, 1583, 10109, 2519, 1…
## $ `1/7/21` <dbl> 4705, 14845, 1614, 1981, 4957, 927, 1598, 10283, 2552, 1…
## $ `1/8/21` <dbl> 4770, 15052, 1634, 2015, 5018, 949, 1610, 10372, 2592, 1…
## $ `1/9/21` <dbl> 4847, 15202, 1648, 2038, 5047, 950, 1625, 10453, 2620, 1…
## $ `1/10/21` <dbl> 4879, 15327, 1658, 2051, 5066, 953, 1632, 10497, 2639, 1…
## $ `1/11/21` <dbl> 4902, 15417, 1663, 2060, 5080, 957, 1637, 10537, 2651, 1…
## $ `1/12/21` <dbl> 4970, 15572, 1679, 2090, 5134, 967, 1649, 10668, 2697, 1…
## $ `1/13/21` <dbl> 4998, 15701, 1685, 2109, 5170, 966, 1651, 10745, 2734, 1…
## $ `1/14/21` <dbl> 5075, 15841, 1696, 2113, 5219, 971, 1669, 10863, 2757, 1…
## $ `1/15/21` <dbl> 5103, 16002, 1712, 2130, 5264, 981, 1679, 10982, 2778, 1…
## $ `1/16/21` <dbl> 5154, 16176, 1723, 2144, 5292, 987, 1684, 11078, 2818, 1…
## $ `1/17/21` <dbl> 5184, 16251, 1729, 2151, 5304, 990, 1696, 11122, 2827, 1…
## $ `1/18/21` <dbl> 5198, 16346, 1730, 2162, 5308, 991, 1702, 11161, 2842, 1…
## $ `1/19/21` <dbl> 5227, 16513, 1738, 2170, 5320, 997, 1707, 11206, 2886, 1…
## $ `1/20/21` <dbl> 5257, 16653, 1760, 2188, 5376, 1011, 1708, 11292, 2931, …
## $ `1/21/21` <dbl> 5270, 16798, 1778, 2198, 5411, 1014, 1713, 11365, 2973, …
## $ `1/22/21` <dbl> 5327, 16981, 1793, 2212, 5439, 1022, 1724, 11441, 3011, …
## $ `1/23/21` <dbl> 5358, 17128, 1805, 2223, 5462, 1033, 1731, 11496, 3034, …
## $ `1/24/21` <dbl> 5376, 17256, 1827, 2223, 5473, 1035, 1744, 11521, 3042, …
## $ `1/25/21` <dbl> 5407, 17333, 1834, 2229, 5485, 1046, 1748, 11555, 3054, …
## $ `1/26/21` <dbl> 5440, 17496, 1882, 2247, 5517, 1058, 1759, 11626, 3085, …
## $ `1/27/21` <dbl> 5499, 17629, 1898, 2261, 5568, 1074, 1766, 11730, 3137, …
## $ `1/28/21` <dbl> 5554, 17779, 1920, 2271, 5612, 1079, 1788, 11833, 3159, …
## $ `1/29/21` <dbl> 5596, 17922, 1931, 2284, 5655, 1075, 1800, 11918, 3174, …
## $ `1/30/21` <dbl> 5596, 17922, 1931, 2284, 5655, 1075, 1800, 11918, 3174, …
## $ `1/31/21` <dbl> 5669, 18126, 1951, 2307, 5713, 1086, 1812, 12011, 3203, …
## $ `2/1/21` <dbl> 5683, 18211, 1956, 2309, 5720, 1089, 1827, 12062, 3210, …
## $ `2/2/21` <dbl> 5723, 18344, 1966, 2319, 5745, 1087, 1833, 12102, 3219, …
## $ `2/3/21` <dbl> 5753, 18418, 1981, 2321, 5768, 1093, 1838, 12179, 3233, …
## $ `2/4/21` <dbl> 5811, 18494, 1989, 2327, 5842, 1107, 1847, 12253, 3239, …
## $ `2/5/21` <dbl> 5824, 18568, 1994, 2331, 5871, 1113, 1853, 12325, 3249, …
## $ `2/6/21` <dbl> 5856, 18668, 2002, 2334, 5908, 1121, 1863, 12368, 3259, …
## $ `2/7/21` <dbl> 5869, 18723, 2008, 2339, 5915, 1128, 1865, 12402, 3263, …
## $ `2/8/21` <dbl> 5881, 18763, 2008, 2346, 5920, 1132, 1868, 12426, 3266, …
## $ `2/9/21` <dbl> 5910, 18824, 2019, 2362, 5929, 1132, 1872, 12477, 3283, …
## $ `2/10/21` <dbl> 5930, 18888, 2024, 2368, 5937, 1131, 1882, 12498, 3291, …
## $ `2/11/21` <dbl> 5970, 18960, 2030, 2377, 5955, 1136, 1886, 12539, 3305, …
## $ `2/12/21` <dbl> 5984, 18994, 2036, 2385, 5953, 1137, 1892, 12577, 3313, …
## $ `2/13/21` <dbl> 6002, 19051, 2040, 2393, 5957, 1139, 1898, 12629, 3318, …
## $ `2/14/21` <dbl> 6023, 19105, 2042, 2395, 5961, 1142, 1902, 12700, 3321, …
## $ `2/15/21` <dbl> 6024, 19136, 2044, 2397, 5973, 1142, 1905, 12725, 3325, …
## $ `2/16/21` <dbl> 6038, 19176, 2055, 2400, 5987, 1145, 1910, 12756, 3336, …
## $ `2/17/21` <dbl> 6050, 19267, 2053, 2399, 5997, 1143, 1924, 12784, 3338, …
## $ `2/18/21` <dbl> 6071, 19324, 2057, 2405, 6008, 1144, 1930, 12833, 3348, …
## $ `2/19/21` <dbl> 6079, 19361, 2061, 2411, 6021, 1147, 1934, 12860, 3358, …
## $ `2/20/21` <dbl> 6092, 19392, 2067, 2414, 6040, 1149, 1938, 12915, 3364, …
## $ `2/21/21` <dbl> 6117, 19433, 2070, 2416, 6042, 1151, 1940, 12940, 3367, …
## $ `2/22/21` <dbl> 6121, 19461, 2074, 2417, 6043, 1153, 1945, 13017, 3367, …
## $ `2/23/21` <dbl> 6143, 19554, 2084, 2432, 6058, 1160, 1948, 13063, 3382, …
## $ st_county <chr> "01001", "01003", "01005", "01007", "01009", "01011", "0…Reshape the infection data file into a tall format that allows for more efficient generation of time-series analysis of data. We are using the pivot_longer function instead of the now depricated gather function frequently referenced in the context of reshaping data.
c19_working_tall <- c19_working_wide %>%
pivot_longer(
-c(UID, st_county, Combined_Key),
names_to = "caldate",
values_to = "count"
)
glimpse(c19_working_tall)## Observations: 1,332,660
## Variables: 5
## $ UID <dbl> 84001001, 84001001, 84001001, 84001001, 84001001, 840010…
## $ Combined_Key <chr> "Autauga, Alabama, US", "Autauga, Alabama, US", "Autauga…
## $ st_county <chr> "01001", "01001", "01001", "01001", "01001", "01001", "0…
## $ caldate <chr> "1/22/20", "1/23/20", "1/24/20", "1/25/20", "1/26/20", "…
## $ count <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…Build a reference table of population density values from a combination of the ACS and LAD data
pop_density <- lad_working %>%
inner_join(acs5_working, by = "st_county") %>%
mutate(
den_total = pop_total/land_area_sqmi,
den_lt20 = pop_lt20/land_area_sqmi,
den_gte65 = pop_gte65/land_area_sqmi
) %>%
select(
-starts_with("pop"),
-starts_with("pct"),
-starts_with("area"),
-id,
pop_total,
pop_lt20,
pop_gte65
)
glimpse(pop_density)## Observations: 3,140
## Variables: 8
## $ st_county <chr> "01001", "01003", "01005", "01007", "01009", "01011", …
## $ land_area_sqmi <dbl> 594.4, 1589.8, 884.9, 622.6, 644.8, 622.8, 776.8, 605.…
## $ den_total <dbl> 92.86, 130.90, 29.14, 36.18, 89.40, 16.62, 25.78, 189.…
## $ den_lt20 <dbl> 24.724, 31.657, 6.725, 8.442, 22.999, 4.082, 6.519, 46…
## $ den_gte65 <dbl> 13.542, 25.579, 5.237, 5.880, 15.871, 2.595, 4.899, 31…
## $ pop_total <dbl> 55200, 208107, 25782, 22527, 57645, 10352, 20025, 1150…
## $ pop_lt20 <dbl> 14697, 50328, 5951, 5256, 14829, 2542, 5064, 28337, 77…
## $ pop_gte65 <dbl> 8050, 40665, 4634, 3661, 10233, 1616, 3806, 19386, 640…Perform an analysis (describe & summarize) and visualize results
group- aggregate values based on one or more grouping variablessummarize- calculate descriptive statistics for aggregated valuesggplot- flexible plotting package for incrementally building data visualizations
Aggregate data across all geographies by calendar date
master_ts <- c19_working_tall %>%
mutate(
caldate = mdy(caldate),
day_num = as.numeric(caldate - min(caldate))
) %>%
group_by(caldate, day_num) %>%
summarize(
ct = sum(count)
) %>%
mutate(
ln_ct = log1p(ct)
) %>%
filter(ct > 0)
glimpse(master_ts)## Observations: 399
## Variables: 4
## Groups: caldate [399]
## $ caldate <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26, …
## $ day_num <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,…
## $ ct <dbl> 1, 1, 2, 2, 5, 5, 5, 6, 6, 8, 8, 8, 11, 11, 11, 12, 12, 12, 1…
## $ ln_ct <dbl> 0.6931, 0.6931, 1.0986, 1.0986, 1.7918, 1.7918, 1.7918, 1.945…master_plot <- ggplot(master_ts, mapping = aes(x = day_num, y = ct)) +
geom_line() +
xlab("Number of Days Since First Confirmed Infection") +
ylab("Confirmed Infection Count")
master_plot + ggtitle("US - Number of confirmed cases")
master_plot + ggtitle("US - Number of confirmed cases") + geom_smooth(method="lm")
master_plot + ggtitle("US - Number of confirmed cases (log scale)") + scale_y_continuous(trans='log2')
ln_plot <- ggplot(master_ts, mapping = aes(x = day_num, y = ln_ct)) +
geom_line() +
xlab("Number of Days") +
ylab("Confirmed Infection Count (ln)")
ln_plot + ggtitle("US - Number of confirmed cases (ln)")
ln_plot + ggtitle("US - Number of confirmed cases (ln)") + geom_smooth(method="lm")
Plot growth by state
# calculate the start date for confirmed infections within the state (based on first confirmed value within a county within the state)
state_start <- c19_working_tall %>%
filter(str_length(st_county) == 5 & count > 0) %>%
mutate(
st_fips = str_sub(st_county, 1, 2),
caldate = mdy(caldate)
) %>%
inner_join(state_fips) %>%
group_by(Areaname) %>%
summarize(
start_date = min(caldate)
)
glimpse(state_start)## Observations: 51
## Variables: 2
## $ Areaname <chr> "ALABAMA", "ALASKA", "ARIZONA", "ARKANSAS", "CALIFORNIA", …
## $ start_date <date> 2020-03-11, 2020-03-13, 2020-01-26, 2020-03-14, 2020-01-2…# build the datafram from which plots will be generated
state_ts <- c19_working_tall %>%
filter(str_length(st_county) == 5 & count > 0) %>%
mutate(
st_fips = str_sub(st_county, 1, 2),
caldate = mdy(caldate)
) %>%
inner_join(state_fips) %>%
inner_join(pop_density) %>%
group_by(Areaname, caldate) %>%
summarize(
ct = sum(count),
pop_total = sum(pop_total),
land_area_sqmi = sum(land_area_sqmi),
den_total = pop_total/land_area_sqmi,
rate_infection = (ct/pop_total) * 100000
) %>%
left_join(state_start, by = "Areaname") %>%
mutate(
ln_ct = log1p(ct),
day_num = as.numeric(caldate - start_date)
)
glimpse(state_ts)## Observations: 18,213
## Variables: 10
## Groups: Areaname [51]
## $ Areaname <chr> "ALABAMA", "ALABAMA", "ALABAMA", "ALABAMA", "ALABAMA",…
## $ caldate <date> 2020-03-11, 2020-03-12, 2020-03-13, 2020-03-14, 2020-…
## $ ct <dbl> 3, 4, 8, 15, 28, 36, 51, 61, 88, 115, 149, 180, 224, 2…
## $ pop_total <dbl> 526206, 883766, 1624870, 1624870, 2203525, 2203525, 24…
## $ land_area_sqmi <dbl> 2666, 3468, 5197, 5197, 8180, 8180, 9417, 10154, 13506…
## $ den_total <dbl> 197.38, 254.87, 312.64, 312.64, 269.39, 269.39, 255.48…
## $ rate_infection <dbl> 0.5701, 0.4526, 0.4923, 0.9232, 1.2707, 1.6337, 2.1198…
## $ start_date <date> 2020-03-11, 2020-03-11, 2020-03-11, 2020-03-11, 2020-…
## $ ln_ct <dbl> 1.386, 1.609, 2.197, 2.773, 3.367, 3.611, 3.951, 4.127…
## $ day_num <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, …state_plot <- ggplot(state_ts, mapping = aes(x = day_num, y = ct, color = Areaname)) +
#geom_hex()+
geom_point(size = .3) +
xlab("Number of Days Since First Confirmed Infection") +
ylab("Confirmed Infection Count") +
theme(legend.position="none") +
theme(legend.text=element_text(size=6))
state_plot + ggtitle("Number of confirmed cases - by state")
state_plot + ggtitle("Number of confirmed cases - by state") + theme_tufte() + theme(legend.position="none")
# note use of geom_jitter() instead of geom_point
ln_plot <- ggplot(state_ts, mapping = aes(x = day_num, y = ln_ct, color = Areaname)) +
#geom_hex()+
geom_jitter(size = .3) +
xlab("Number of Days Since First Confirmed Infection (ln)") +
ylab("Confirmed Infection Count (ln)") +
ggtitle("Number of confirmed cases by date") +
theme(legend.position="none") +
theme(legend.text=element_text(size=6))
ln_plot + ggtitle("Number of confirmed cases (ln) - by state")
ln_plot + ggtitle("Number of confirmed cases (ln) - by state") + theme_tufte() + theme(legend.position="none")
What are the states that are showing long growth curves (is there a problem?)
# what are the states that have the long (and early slow) growth curves?
my_colors <- brewer.pal(8, "Dark2")
state_density_data_10d <- state_ts %>%
filter(day_num == 10) %>%
group_by(Areaname, start_date) %>%
summarize(
max_days = max(day_num),
max_infection = max(ct),
max_rate_infection = max(rate_infection),
avg_density = mean(den_total)
) %>%
arrange(desc(max_rate_infection))
state_density_data_20d <- state_ts %>%
filter(day_num == 20) %>%
group_by(Areaname, start_date) %>%
summarize(
max_days = max(day_num),
max_infection = max(ct),
max_rate_infection = max(rate_infection),
avg_density = mean(den_total)
) %>%
arrange(desc(max_rate_infection))
state_density_data_30d <- state_ts %>%
filter(day_num == 30) %>%
group_by(Areaname, start_date) %>%
summarize(
max_days = max(day_num),
max_infection = max(ct),
max_rate_infection = max(rate_infection),
avg_density = mean(den_total)
) %>%
arrange(desc(max_rate_infection))
state_density_data_40d <- state_ts %>%
filter(day_num == 40) %>%
group_by(Areaname, start_date) %>%
summarize(
max_days = max(day_num),
max_infection = max(ct),
max_rate_infection = max(rate_infection),
avg_density = mean(den_total)
) %>%
arrange(desc(max_rate_infection))
state_density_data_50d <- state_ts %>%
filter(day_num == 50) %>%
group_by(Areaname, start_date) %>%
summarize(
max_days = max(day_num),
max_infection = max(ct),
max_rate_infection = max(rate_infection),
avg_density = mean(den_total)
) %>%
arrange(desc(max_rate_infection))
state_density_data_60d <- state_ts %>%
filter(day_num == 60) %>%
group_by(Areaname, start_date) %>%
summarize(
max_days = max(day_num),
max_infection = max(ct),
max_rate_infection = max(rate_infection),
avg_density = mean(den_total)
) %>%
arrange(desc(max_rate_infection))
state_density_data_70d <- state_ts %>%
filter(day_num == 70) %>%
group_by(Areaname, start_date) %>%
summarize(
max_days = max(day_num),
max_infection = max(ct),
max_rate_infection = max(rate_infection),
avg_density = mean(den_total)
) %>%
arrange(desc(max_rate_infection))
state_density_data_80d <- state_ts %>%
filter(day_num == 80) %>%
group_by(Areaname, start_date) %>%
summarize(
max_days = max(day_num),
max_infection = max(ct),
max_rate_infection = max(rate_infection),
avg_density = mean(den_total)
) %>%
arrange(desc(max_rate_infection))
base_plot <- ggplot() +
geom_jitter(data = state_density_data_10d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[1], shape = 1) +
geom_jitter(data = state_density_data_20d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[2], shape = 2) +
geom_jitter(data = state_density_data_30d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[3], shape = 3) +
geom_jitter(data = state_density_data_40d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[4], shape = 4) +
geom_jitter(data = state_density_data_50d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[4], shape = 5) +
geom_jitter(data = state_density_data_60d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[4], shape = 6) +
geom_jitter(data = state_density_data_70d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[4], shape = 7) +
geom_jitter(data = state_density_data_80d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[4], shape = 8) +
theme_tufte() +
xlab("Infection rate (cases/100,000) ") +
ylab("Average population density (persons/mi^2)") +
ggtitle("Infection Rate vs. Population Density Day 10, 20, 30, 40, 50, 60, 70")
base_plot
base_plot +
scale_y_continuous(trans='log2')+
scale_x_continuous(trans="log2")
print(state_density_data_10d)## # A tibble: 51 x 6
## # Groups: Areaname [51]
## Areaname start_date max_days max_infection max_rate_infect… avg_density
## <chr> <date> <dbl> <dbl> <dbl> <dbl>
## 1 DISTRICT OF C… 2020-03-16 10 231 33.7 11212.
## 2 LOUISIANA 2020-03-11 10 754 19.0 173.
## 3 ARKANSAS 2020-03-14 10 206 9.10 87.2
## 4 MISSISSIPPI 2020-03-12 10 207 8.63 78.8
## 5 WEST VIRGINIA 2020-03-18 10 96 8.23 135.
## 6 MAINE 2020-03-12 10 81 8.09 107.
## 7 MICHIGAN 2020-03-11 10 625 7.72 450.
## 8 NORTH DAKOTA 2020-03-12 10 28 7.31 40.2
## 9 WYOMING 2020-03-12 10 26 7.15 8.39
## 10 IDAHO 2020-03-14 10 81 6.98 61.4
## # … with 41 more rowsstate_density_data_10d %>%
kable() %>%
kable_styling()| Areaname | start_date | max_days | max_infection | max_rate_infection | avg_density |
|---|---|---|---|---|---|
| DISTRICT OF COLUMBIA | 2020-03-16 | 10 | 231 | 33.7474 | 11212.088 |
| LOUISIANA | 2020-03-11 | 10 | 754 | 19.0192 | 172.866 |
| ARKANSAS | 2020-03-14 | 10 | 206 | 9.1030 | 87.182 |
| MISSISSIPPI | 2020-03-12 | 10 | 207 | 8.6310 | 78.777 |
| WEST VIRGINIA | 2020-03-18 | 10 | 96 | 8.2317 | 134.540 |
| MAINE | 2020-03-12 | 10 | 81 | 8.0883 | 107.163 |
| MICHIGAN | 2020-03-11 | 10 | 625 | 7.7183 | 449.891 |
| NORTH DAKOTA | 2020-03-12 | 10 | 28 | 7.3053 | 40.166 |
| WYOMING | 2020-03-12 | 10 | 26 | 7.1477 | 8.387 |
| IDAHO | 2020-03-14 | 10 | 81 | 6.9832 | 61.383 |
| ALASKA | 2020-03-13 | 10 | 39 | 6.4268 | 10.019 |
| MONTANA | 2020-03-14 | 10 | 45 | 6.1862 | 24.812 |
| CONNECTICUT | 2020-03-10 | 10 | 194 | 5.4167 | 739.620 |
| WISCONSIN | 2020-03-10 | 10 | 219 | 4.7780 | 231.706 |
| DELAWARE | 2020-03-11 | 10 | 45 | 4.7394 | 487.283 |
| NEW MEXICO | 2020-03-11 | 10 | 55 | 4.6286 | 39.364 |
| ALABAMA | 2020-03-11 | 10 | 149 | 4.5330 | 185.927 |
| SOUTH DAKOTA | 2020-03-11 | 10 | 14 | 3.9310 | 47.401 |
| IOWA | 2020-03-09 | 10 | 44 | 3.7620 | 144.903 |
| VERMONT | 2020-03-08 | 10 | 13 | 3.4414 | 87.412 |
| NEBRASKA | 2020-03-06 | 10 | 18 | 3.0552 | 295.425 |
| COLORADO | 2020-03-05 | 10 | 136 | 2.7781 | 157.500 |
| NEW YORK | 2020-03-03 | 10 | 379 | 2.4986 | 1432.442 |
| UTAH | 2020-03-07 | 10 | 35 | 2.1907 | 144.850 |
| KANSAS | 2020-03-08 | 10 | 22 | 2.0451 | 231.659 |
| OHIO | 2020-03-10 | 10 | 173 | 2.0375 | 588.599 |
| KENTUCKY | 2020-03-06 | 10 | 21 | 1.7022 | 579.894 |
| VIRGINIA | 2020-03-08 | 10 | 79 | 1.6710 | 1029.982 |
| MINNESOTA | 2020-03-06 | 10 | 53 | 1.5532 | 494.382 |
| SOUTH CAROLINA | 2020-03-07 | 10 | 47 | 1.5463 | 322.846 |
| NEW HAMPSHIRE | 2020-03-02 | 10 | 6 | 1.5192 | 164.321 |
| PENNSYLVANIA | 2020-03-06 | 10 | 79 | 1.1220 | 905.944 |
| INDIANA | 2020-03-06 | 10 | 25 | 1.0234 | 463.620 |
| OREGON | 2020-02-29 | 10 | 14 | 1.0122 | 73.251 |
| TENNESSEE | 2020-03-05 | 10 | 32 | 0.9968 | 684.851 |
| GEORGIA | 2020-03-03 | 10 | 42 | 0.9514 | 679.700 |
| NEW JERSEY | 2020-03-05 | 10 | 72 | 0.9274 | 1712.438 |
| OKLAHOMA | 2020-03-07 | 10 | 17 | 0.8473 | 352.663 |
| MARYLAND | 2020-03-06 | 10 | 42 | 0.8181 | 1118.423 |
| HAWAII | 2020-03-07 | 10 | 10 | 0.8168 | 513.931 |
| RHODE ISLAND | 2020-03-01 | 10 | 5 | 0.7880 | 1549.531 |
| NEVADA | 2020-03-05 | 10 | 20 | 0.7716 | 182.619 |
| NORTH CAROLINA | 2020-03-02 | 10 | 17 | 0.5176 | 598.802 |
| TEXAS | 2020-03-05 | 10 | 64 | 0.3592 | 1118.202 |
| MISSOURI | 2020-03-03 | 10 | 5 | 0.3128 | 1283.834 |
| FLORIDA | 2020-03-02 | 10 | 30 | 0.2718 | 744.295 |
| MASSACHUSETTS | 2020-01-29 | 10 | 2 | 0.1239 | 1028.763 |
| WASHINGTON | 2020-01-22 | 10 | 1 | 0.0462 | 1022.541 |
| CALIFORNIA | 2020-01-26 | 10 | 6 | 0.0394 | 2025.153 |
| ILLINOIS | 2020-01-24 | 10 | 2 | 0.0383 | 5525.815 |
| ARIZONA | 2020-01-26 | 10 | 1 | 0.0235 | 462.375 |
# Another way to do this - by combining out three dataframes into 1 with a new column that identifies the day number for the values
plot_data <- bind_rows(
mutate(state_density_data_10d, day_num = "d10"),
mutate(state_density_data_20d, day_num = "d20"),
mutate(state_density_data_30d, day_num = "d30"),
mutate(state_density_data_40d, day_num = "d40"),
mutate(state_density_data_50d, day_num = "d50"),
mutate(state_density_data_60d, day_num = "d60"),
mutate(state_density_data_70d, day_num = "d70"),
mutate(state_density_data_80d, day_num = "d80")
)
base_plot <- ggplot(plot_data, mapping = aes(x = max_rate_infection, y = avg_density, color = day_num, shape = day_num)) +
geom_jitter() +
scale_color_brewer(palette = "Dark2") +
theme_tufte() +
xlab("Infection rate (cases/100,000) ") +
ylab("Average population density (persons/mi^2)") +
ggtitle("Infection Rate vs. Population Density Day 10, 20, 30, 40, 50, 60, 70")
base_plot## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 8. Consider
## specifying shapes manually if you must have them.## Warning: Removed 102 rows containing missing values (geom_point).
base_plot + scale_shape_manual(values = c(1, 2, 3, 4, 5, 6, 7, 8))
base_plot +
scale_shape_manual(values = c(1, 2, 3, 4, 5, 6, 7, 8)) +
scale_y_continuous(trans='log2') +
scale_x_continuous(trans="log2")
print(plot_data)## # A tibble: 408 x 7
## # Groups: Areaname [51]
## Areaname start_date max_days max_infection max_rate_infect… avg_density
## <chr> <date> <dbl> <dbl> <dbl> <dbl>
## 1 DISTRIC… 2020-03-16 10 231 33.7 11212.
## 2 LOUISIA… 2020-03-11 10 754 19.0 173.
## 3 ARKANSAS 2020-03-14 10 206 9.10 87.2
## 4 MISSISS… 2020-03-12 10 207 8.63 78.8
## 5 WEST VI… 2020-03-18 10 96 8.23 135.
## 6 MAINE 2020-03-12 10 81 8.09 107.
## 7 MICHIGAN 2020-03-11 10 625 7.72 450.
## 8 NORTH D… 2020-03-12 10 28 7.31 40.2
## 9 WYOMING 2020-03-12 10 26 7.15 8.39
## 10 IDAHO 2020-03-14 10 81 6.98 61.4
## # … with 398 more rows, and 1 more variable: day_num <chr>plot_data %>%
kable() %>%
kable_styling()| Areaname | start_date | max_days | max_infection | max_rate_infection | avg_density | day_num |
|---|---|---|---|---|---|---|
| DISTRICT OF COLUMBIA | 2020-03-16 | 10 | 231 | 33.7474 | 11212.088 | d10 |
| LOUISIANA | 2020-03-11 | 10 | 754 | 19.0192 | 172.866 | d10 |
| ARKANSAS | 2020-03-14 | 10 | 206 | 9.1030 | 87.182 | d10 |
| MISSISSIPPI | 2020-03-12 | 10 | 207 | 8.6310 | 78.777 | d10 |
| WEST VIRGINIA | 2020-03-18 | 10 | 96 | 8.2317 | 134.540 | d10 |
| MAINE | 2020-03-12 | 10 | 81 | 8.0883 | 107.163 | d10 |
| MICHIGAN | 2020-03-11 | 10 | 625 | 7.7183 | 449.891 | d10 |
| NORTH DAKOTA | 2020-03-12 | 10 | 28 | 7.3053 | 40.166 | d10 |
| WYOMING | 2020-03-12 | 10 | 26 | 7.1477 | 8.387 | d10 |
| IDAHO | 2020-03-14 | 10 | 81 | 6.9832 | 61.383 | d10 |
| ALASKA | 2020-03-13 | 10 | 39 | 6.4268 | 10.019 | d10 |
| MONTANA | 2020-03-14 | 10 | 45 | 6.1862 | 24.812 | d10 |
| CONNECTICUT | 2020-03-10 | 10 | 194 | 5.4167 | 739.620 | d10 |
| WISCONSIN | 2020-03-10 | 10 | 219 | 4.7780 | 231.706 | d10 |
| DELAWARE | 2020-03-11 | 10 | 45 | 4.7394 | 487.283 | d10 |
| NEW MEXICO | 2020-03-11 | 10 | 55 | 4.6286 | 39.364 | d10 |
| ALABAMA | 2020-03-11 | 10 | 149 | 4.5330 | 185.927 | d10 |
| SOUTH DAKOTA | 2020-03-11 | 10 | 14 | 3.9310 | 47.401 | d10 |
| IOWA | 2020-03-09 | 10 | 44 | 3.7620 | 144.903 | d10 |
| VERMONT | 2020-03-08 | 10 | 13 | 3.4414 | 87.412 | d10 |
| NEBRASKA | 2020-03-06 | 10 | 18 | 3.0552 | 295.425 | d10 |
| COLORADO | 2020-03-05 | 10 | 136 | 2.7781 | 157.500 | d10 |
| NEW YORK | 2020-03-03 | 10 | 379 | 2.4986 | 1432.442 | d10 |
| UTAH | 2020-03-07 | 10 | 35 | 2.1907 | 144.850 | d10 |
| KANSAS | 2020-03-08 | 10 | 22 | 2.0451 | 231.659 | d10 |
| OHIO | 2020-03-10 | 10 | 173 | 2.0375 | 588.599 | d10 |
| KENTUCKY | 2020-03-06 | 10 | 21 | 1.7022 | 579.894 | d10 |
| VIRGINIA | 2020-03-08 | 10 | 79 | 1.6710 | 1029.982 | d10 |
| MINNESOTA | 2020-03-06 | 10 | 53 | 1.5532 | 494.382 | d10 |
| SOUTH CAROLINA | 2020-03-07 | 10 | 47 | 1.5463 | 322.846 | d10 |
| NEW HAMPSHIRE | 2020-03-02 | 10 | 6 | 1.5192 | 164.321 | d10 |
| PENNSYLVANIA | 2020-03-06 | 10 | 79 | 1.1220 | 905.944 | d10 |
| INDIANA | 2020-03-06 | 10 | 25 | 1.0234 | 463.620 | d10 |
| OREGON | 2020-02-29 | 10 | 14 | 1.0122 | 73.251 | d10 |
| TENNESSEE | 2020-03-05 | 10 | 32 | 0.9968 | 684.851 | d10 |
| GEORGIA | 2020-03-03 | 10 | 42 | 0.9514 | 679.700 | d10 |
| NEW JERSEY | 2020-03-05 | 10 | 72 | 0.9274 | 1712.438 | d10 |
| OKLAHOMA | 2020-03-07 | 10 | 17 | 0.8473 | 352.663 | d10 |
| MARYLAND | 2020-03-06 | 10 | 42 | 0.8181 | 1118.423 | d10 |
| HAWAII | 2020-03-07 | 10 | 10 | 0.8168 | 513.931 | d10 |
| RHODE ISLAND | 2020-03-01 | 10 | 5 | 0.7880 | 1549.531 | d10 |
| NEVADA | 2020-03-05 | 10 | 20 | 0.7716 | 182.619 | d10 |
| NORTH CAROLINA | 2020-03-02 | 10 | 17 | 0.5176 | 598.802 | d10 |
| TEXAS | 2020-03-05 | 10 | 64 | 0.3592 | 1118.202 | d10 |
| MISSOURI | 2020-03-03 | 10 | 5 | 0.3128 | 1283.834 | d10 |
| FLORIDA | 2020-03-02 | 10 | 30 | 0.2718 | 744.295 | d10 |
| MASSACHUSETTS | 2020-01-29 | 10 | 2 | 0.1239 | 1028.763 | d10 |
| WASHINGTON | 2020-01-22 | 10 | 1 | 0.0462 | 1022.541 | d10 |
| CALIFORNIA | 2020-01-26 | 10 | 6 | 0.0394 | 2025.153 | d10 |
| ILLINOIS | 2020-01-24 | 10 | 2 | 0.0383 | 5525.815 | d10 |
| ARIZONA | 2020-01-26 | 10 | 1 | 0.0235 | 462.375 | d10 |
| DISTRICT OF COLUMBIA | 2020-03-16 | 20 | 1002 | 146.3846 | 11212.088 | d20 |
| LOUISIANA | 2020-03-11 | 20 | 5215 | 112.6130 | 114.549 | d20 |
| NEW YORK | 2020-03-03 | 20 | 20777 | 109.1947 | 512.861 | d20 |
| MICHIGAN | 2020-03-11 | 20 | 7838 | 80.2733 | 218.716 | d20 |
| CONNECTICUT | 2020-03-10 | 20 | 2395 | 66.8713 | 739.620 | d20 |
| IDAHO | 2020-03-14 | 20 | 891 | 56.0670 | 26.914 | d20 |
| NEW JERSEY | 2020-03-05 | 20 | 3472 | 39.0910 | 1207.721 | d20 |
| MISSISSIPPI | 2020-03-12 | 20 | 1073 | 36.7035 | 66.242 | d20 |
| DELAWARE | 2020-03-11 | 20 | 319 | 33.5968 | 487.283 | d20 |
| VERMONT | 2020-03-08 | 20 | 204 | 33.3440 | 72.221 | d20 |
| MONTANA | 2020-03-14 | 20 | 243 | 27.9825 | 15.741 | d20 |
| WEST VIRGINIA | 2020-03-18 | 20 | 412 | 26.0095 | 96.097 | d20 |
| WYOMING | 2020-03-12 | 20 | 130 | 25.9998 | 6.594 | d20 |
| MAINE | 2020-03-12 | 20 | 297 | 25.5721 | 72.312 | d20 |
| ALASKA | 2020-03-13 | 20 | 150 | 24.4997 | 2.971 | d20 |
| ARKANSAS | 2020-03-14 | 20 | 657 | 24.0688 | 68.126 | d20 |
| WISCONSIN | 2020-03-10 | 20 | 1230 | 23.9688 | 154.805 | d20 |
| ALABAMA | 2020-03-11 | 20 | 1063 | 23.0534 | 105.683 | d20 |
| NORTH DAKOTA | 2020-03-12 | 20 | 142 | 23.0285 | 17.083 | d20 |
| COLORADO | 2020-03-05 | 20 | 1069 | 20.0354 | 94.081 | d20 |
| UTAH | 2020-03-07 | 20 | 438 | 18.6492 | 151.922 | d20 |
| OHIO | 2020-03-10 | 20 | 1933 | 17.3785 | 339.450 | d20 |
| NEW MEXICO | 2020-03-11 | 20 | 315 | 16.7454 | 27.497 | d20 |
| SOUTH DAKOTA | 2020-03-11 | 20 | 108 | 15.2156 | 23.318 | d20 |
| PENNSYLVANIA | 2020-03-06 | 20 | 1795 | 14.8609 | 366.841 | d20 |
| IOWA | 2020-03-09 | 20 | 336 | 13.3736 | 82.343 | d20 |
| TENNESSEE | 2020-03-05 | 20 | 735 | 12.8598 | 235.944 | d20 |
| KANSAS | 2020-03-08 | 20 | 266 | 11.8573 | 99.332 | d20 |
| SOUTH CAROLINA | 2020-03-07 | 20 | 542 | 11.3921 | 178.289 | d20 |
| INDIANA | 2020-03-06 | 20 | 645 | 11.0780 | 245.319 | d20 |
| NEVADA | 2020-03-05 | 20 | 308 | 10.8318 | 54.264 | d20 |
| VIRGINIA | 2020-03-08 | 20 | 740 | 10.2787 | 322.858 | d20 |
| MARYLAND | 2020-03-06 | 20 | 583 | 9.8827 | 674.788 | d20 |
| OKLAHOMA | 2020-03-07 | 20 | 322 | 9.7079 | 106.538 | d20 |
| GEORGIA | 2020-03-03 | 20 | 727 | 8.6310 | 357.972 | d20 |
| MINNESOTA | 2020-03-06 | 20 | 344 | 7.6029 | 161.825 | d20 |
| HAWAII | 2020-03-07 | 20 | 100 | 7.0326 | 221.812 | d20 |
| MISSOURI | 2020-03-03 | 20 | 254 | 6.1712 | 271.594 | d20 |
| NEW HAMPSHIRE | 2020-03-02 | 20 | 73 | 5.8356 | 185.113 | d20 |
| NEBRASKA | 2020-03-06 | 20 | 74 | 5.6982 | 128.097 | d20 |
| RHODE ISLAND | 2020-03-01 | 20 | 54 | 5.1107 | 1022.045 | d20 |
| KENTUCKY | 2020-03-06 | 20 | 143 | 5.1029 | 234.846 | d20 |
| TEXAS | 2020-03-05 | 20 | 1248 | 4.9831 | 281.553 | d20 |
| FLORIDA | 2020-03-02 | 20 | 1003 | 4.9803 | 508.701 | d20 |
| NORTH CAROLINA | 2020-03-02 | 20 | 325 | 3.8771 | 329.801 | d20 |
| OREGON | 2020-02-29 | 20 | 114 | 3.1949 | 89.781 | d20 |
| MASSACHUSETTS | 2020-01-29 | 20 | 2 | 0.1239 | 1028.763 | d20 |
| WASHINGTON | 2020-01-22 | 20 | 1 | 0.0462 | 1022.541 | d20 |
| CALIFORNIA | 2020-01-26 | 20 | 8 | 0.0431 | 1580.608 | d20 |
| ILLINOIS | 2020-01-24 | 20 | 2 | 0.0383 | 5525.815 | d20 |
| ARIZONA | 2020-01-26 | 20 | 1 | 0.0235 | 462.375 | d20 |
| NEW YORK | 2020-03-03 | 30 | 100780 | 513.7000 | 416.294 | d30 |
| LOUISIANA | 2020-03-11 | 30 | 19199 | 412.0886 | 109.362 | d30 |
| NEW JERSEY | 2020-03-05 | 30 | 30189 | 339.8956 | 1207.721 | d30 |
| DISTRICT OF COLUMBIA | 2020-03-16 | 30 | 2197 | 320.9651 | 11212.088 | d30 |
| CONNECTICUT | 2020-03-10 | 30 | 9392 | 262.2362 | 739.620 | d30 |
| MICHIGAN | 2020-03-11 | 30 | 23058 | 233.9800 | 198.702 | d30 |
| DELAWARE | 2020-03-11 | 30 | 1317 | 138.7053 | 487.283 | d30 |
| VERMONT | 2020-03-08 | 30 | 572 | 92.4416 | 72.345 | d30 |
| PENNSYLVANIA | 2020-03-06 | 30 | 11589 | 91.1336 | 293.935 | d30 |
| IDAHO | 2020-03-14 | 30 | 1426 | 88.4887 | 25.578 | d30 |
| MISSISSIPPI | 2020-03-12 | 30 | 2642 | 88.4371 | 64.232 | d30 |
| COLORADO | 2020-03-05 | 30 | 4361 | 79.4398 | 59.741 | d30 |
| SOUTH DAKOTA | 2020-03-11 | 30 | 535 | 73.0502 | 20.782 | d30 |
| INDIANA | 2020-03-06 | 30 | 4411 | 66.9902 | 192.137 | d30 |
| ALABAMA | 2020-03-11 | 30 | 3112 | 64.3216 | 96.627 | d30 |
| UTAH | 2020-03-07 | 30 | 1596 | 60.3389 | 48.767 | d30 |
| MARYLAND | 2020-03-06 | 30 | 3617 | 60.2488 | 618.450 | d30 |
| NEVADA | 2020-03-05 | 30 | 1708 | 59.5100 | 40.471 | d30 |
| NEW MEXICO | 2020-03-11 | 30 | 1081 | 53.2866 | 21.442 | d30 |
| WISCONSIN | 2020-03-10 | 30 | 2886 | 51.9281 | 122.797 | d30 |
| OHIO | 2020-03-10 | 30 | 5512 | 47.7188 | 295.103 | d30 |
| WYOMING | 2020-03-12 | 30 | 261 | 47.0981 | 6.194 | d30 |
| ARKANSAS | 2020-03-14 | 30 | 1374 | 46.8192 | 60.085 | d30 |
| MAINE | 2020-03-12 | 30 | 613 | 46.5832 | 48.952 | d30 |
| TENNESSEE | 2020-03-05 | 30 | 3041 | 46.5469 | 168.180 | d30 |
| GEORGIA | 2020-03-03 | 30 | 4570 | 45.1924 | 194.037 | d30 |
| SOUTH CAROLINA | 2020-03-07 | 30 | 2232 | 45.0370 | 164.864 | d30 |
| NORTH DAKOTA | 2020-03-12 | 30 | 293 | 43.8999 | 14.789 | d30 |
| WEST VIRGINIA | 2020-03-18 | 30 | 775 | 43.8063 | 85.002 | d30 |
| MONTANA | 2020-03-14 | 30 | 394 | 43.1704 | 12.898 | d30 |
| ALASKA | 2020-03-13 | 30 | 276 | 42.8809 | 2.338 | d30 |
| VIRGINIA | 2020-03-08 | 30 | 3335 | 40.5715 | 230.979 | d30 |
| IOWA | 2020-03-09 | 30 | 1145 | 38.7308 | 64.335 | d30 |
| MISSOURI | 2020-03-03 | 30 | 2001 | 36.5516 | 126.857 | d30 |
| OKLAHOMA | 2020-03-07 | 30 | 1327 | 35.3028 | 74.331 | d30 |
| KANSAS | 2020-03-08 | 30 | 912 | 34.5613 | 63.526 | d30 |
| FLORIDA | 2020-03-02 | 30 | 6953 | 34.0858 | 439.637 | d30 |
| RHODE ISLAND | 2020-03-01 | 30 | 304 | 28.7712 | 1022.045 | d30 |
| NEW HAMPSHIRE | 2020-03-02 | 30 | 367 | 27.9814 | 183.234 | d30 |
| HAWAII | 2020-03-07 | 30 | 376 | 26.4425 | 221.812 | d30 |
| TEXAS | 2020-03-05 | 30 | 6596 | 24.3507 | 191.607 | d30 |
| KENTUCKY | 2020-03-06 | 30 | 921 | 24.1035 | 140.952 | d30 |
| NEBRASKA | 2020-03-06 | 30 | 363 | 22.5177 | 59.554 | d30 |
| NORTH CAROLINA | 2020-03-02 | 30 | 1855 | 19.0079 | 238.267 | d30 |
| MINNESOTA | 2020-03-06 | 30 | 928 | 18.0604 | 96.511 | d30 |
| OREGON | 2020-02-29 | 30 | 606 | 15.6294 | 77.722 | d30 |
| MASSACHUSETTS | 2020-01-29 | 30 | 2 | 0.1239 | 1028.763 | d30 |
| CALIFORNIA | 2020-01-26 | 30 | 10 | 0.0495 | 1241.352 | d30 |
| WASHINGTON | 2020-01-22 | 30 | 1 | 0.0462 | 1022.541 | d30 |
| ILLINOIS | 2020-01-24 | 30 | 2 | 0.0383 | 5525.815 | d30 |
| ARIZONA | 2020-01-26 | 30 | 1 | 0.0235 | 462.375 | d30 |
| NEW YORK | 2020-03-03 | 40 | 200635 | 1022.6851 | 416.294 | d40 |
| NEW JERSEY | 2020-03-05 | 40 | 67122 | 755.7214 | 1207.721 | d40 |
| DISTRICT OF COLUMBIA | 2020-03-16 | 40 | 3699 | 540.3960 | 11212.088 | d40 |
| LOUISIANA | 2020-03-11 | 40 | 24464 | 524.5715 | 107.944 | d40 |
| CONNECTICUT | 2020-03-10 | 40 | 17433 | 486.7508 | 739.620 | d40 |
| MICHIGAN | 2020-03-11 | 40 | 32611 | 328.7241 | 191.855 | d40 |
| DELAWARE | 2020-03-11 | 40 | 2714 | 285.8362 | 487.283 | d40 |
| SOUTH DAKOTA | 2020-03-11 | 40 | 1684 | 224.7014 | 19.263 | d40 |
| PENNSYLVANIA | 2020-03-06 | 40 | 26753 | 209.1519 | 285.883 | d40 |
| MARYLAND | 2020-03-06 | 40 | 10032 | 167.1043 | 618.450 | d40 |
| MISSISSIPPI | 2020-03-12 | 40 | 4716 | 157.8612 | 64.232 | d40 |
| COLORADO | 2020-03-05 | 40 | 7831 | 142.1835 | 58.793 | d40 |
| INDIANA | 2020-03-06 | 40 | 8960 | 134.9921 | 185.268 | d40 |
| RHODE ISLAND | 2020-03-01 | 40 | 1319 | 124.8331 | 1022.045 | d40 |
| VERMONT | 2020-03-08 | 40 | 769 | 123.0445 | 67.809 | d40 |
| IDAHO | 2020-03-14 | 40 | 1836 | 114.7532 | 25.916 | d40 |
| GEORGIA | 2020-03-03 | 40 | 11234 | 109.2580 | 180.412 | d40 |
| NEVADA | 2020-03-05 | 40 | 3134 | 108.9980 | 35.258 | d40 |
| ALABAMA | 2020-03-11 | 40 | 5163 | 106.1324 | 96.054 | d40 |
| OHIO | 2020-03-10 | 40 | 11602 | 99.7698 | 287.497 | d40 |
| UTAH | 2020-03-07 | 40 | 2560 | 96.7842 | 48.767 | d40 |
| NORTH DAKOTA | 2020-03-12 | 40 | 644 | 96.0696 | 14.537 | d40 |
| NEW MEXICO | 2020-03-11 | 40 | 1971 | 95.4169 | 19.962 | d40 |
| VIRGINIA | 2020-03-08 | 40 | 7491 | 90.0260 | 225.469 | d40 |
| FLORIDA | 2020-03-02 | 40 | 18492 | 89.7751 | 384.116 | d40 |
| IOWA | 2020-03-09 | 40 | 2512 | 84.0680 | 63.122 | d40 |
| ARKANSAS | 2020-03-14 | 40 | 2472 | 83.3991 | 59.169 | d40 |
| TENNESSEE | 2020-03-05 | 40 | 5431 | 82.4632 | 164.077 | d40 |
| SOUTH CAROLINA | 2020-03-07 | 40 | 3931 | 79.3192 | 164.864 | d40 |
| WYOMING | 2020-03-12 | 40 | 443 | 78.2598 | 6.112 | d40 |
| WISCONSIN | 2020-03-10 | 40 | 4346 | 76.9344 | 116.296 | d40 |
| MISSOURI | 2020-03-03 | 40 | 4256 | 74.2045 | 108.709 | d40 |
| MAINE | 2020-03-12 | 40 | 887 | 66.5510 | 43.213 | d40 |
| NEW HAMPSHIRE | 2020-03-02 | 40 | 885 | 65.8667 | 150.081 | d40 |
| KANSAS | 2020-03-08 | 40 | 1730 | 63.5345 | 54.421 | d40 |
| OKLAHOMA | 2020-03-07 | 40 | 2357 | 61.7884 | 68.261 | d40 |
| WEST VIRGINIA | 2020-03-18 | 40 | 1063 | 59.5222 | 80.839 | d40 |
| TEXAS | 2020-03-05 | 40 | 15088 | 54.9106 | 158.604 | d40 |
| NEBRASKA | 2020-03-06 | 40 | 882 | 51.1141 | 46.825 | d40 |
| ALASKA | 2020-03-13 | 40 | 340 | 50.9579 | 2.189 | d40 |
| KENTUCKY | 2020-03-06 | 40 | 2132 | 50.2669 | 120.513 | d40 |
| MONTANA | 2020-03-14 | 40 | 442 | 48.1111 | 12.693 | d40 |
| NORTH CAROLINA | 2020-03-02 | 40 | 4535 | 45.2865 | 222.259 | d40 |
| HAWAII | 2020-03-07 | 40 | 523 | 36.7804 | 221.812 | d40 |
| MINNESOTA | 2020-03-06 | 40 | 1807 | 33.9916 | 83.833 | d40 |
| OREGON | 2020-02-29 | 40 | 1321 | 33.3493 | 57.952 | d40 |
| MASSACHUSETTS | 2020-01-29 | 40 | 96 | 1.9934 | 1146.017 | d40 |
| WASHINGTON | 2020-01-22 | 40 | 18 | 0.6102 | 701.877 | d40 |
| CALIFORNIA | 2020-01-26 | 40 | 59 | 0.2296 | 1156.992 | d40 |
| ILLINOIS | 2020-01-24 | 40 | 4 | 0.0766 | 5525.815 | d40 |
| ARIZONA | 2020-01-26 | 40 | 2 | 0.0470 | 462.375 | d40 |
| NEW YORK | 2020-03-03 | 50 | 266291 | 1357.3496 | 416.294 | d50 |
| NEW JERSEY | 2020-03-05 | 50 | 101664 | 1144.6270 | 1207.721 | d50 |
| DISTRICT OF COLUMBIA | 2020-03-16 | 50 | 5322 | 777.5041 | 11212.088 | d50 |
| CONNECTICUT | 2020-03-10 | 50 | 26260 | 733.2115 | 739.620 | d50 |
| LOUISIANA | 2020-03-11 | 50 | 27940 | 599.1059 | 107.944 | d50 |
| DELAWARE | 2020-03-11 | 50 | 4709 | 495.9478 | 487.283 | d50 |
| MICHIGAN | 2020-03-11 | 50 | 41904 | 422.3990 | 191.855 | d50 |
| RHODE ISLAND | 2020-03-01 | 50 | 4229 | 400.2419 | 1022.045 | d50 |
| PENNSYLVANIA | 2020-03-06 | 50 | 41153 | 321.7295 | 285.883 | d50 |
| SOUTH DAKOTA | 2020-03-11 | 50 | 2448 | 318.5053 | 17.135 | d50 |
| MARYLAND | 2020-03-06 | 50 | 17766 | 295.9306 | 618.450 | d50 |
| MISSISSIPPI | 2020-03-12 | 50 | 7212 | 241.4112 | 64.232 | d50 |
| COLORADO | 2020-03-05 | 50 | 12153 | 220.6559 | 58.793 | d50 |
| INDIANA | 2020-03-06 | 50 | 14399 | 216.9365 | 185.268 | d50 |
| IOWA | 2020-03-09 | 50 | 6363 | 210.9754 | 61.643 | d50 |
| GEORGIA | 2020-03-03 | 50 | 19544 | 189.8801 | 180.022 | d50 |
| UTAH | 2020-03-07 | 50 | 3788 | 171.9188 | 105.662 | d50 |
| NEW MEXICO | 2020-03-11 | 50 | 3411 | 163.8769 | 18.321 | d50 |
| VIRGINIA | 2020-03-08 | 50 | 13538 | 161.9560 | 221.065 | d50 |
| NORTH DAKOTA | 2020-03-12 | 50 | 1107 | 160.6038 | 13.719 | d50 |
| NEBRASKA | 2020-03-06 | 50 | 2719 | 152.0761 | 40.738 | d50 |
| NEVADA | 2020-03-05 | 50 | 4333 | 149.1920 | 34.132 | d50 |
| OHIO | 2020-03-10 | 50 | 17303 | 148.6272 | 284.916 | d50 |
| ALABAMA | 2020-03-11 | 50 | 7187 | 147.7384 | 96.054 | d50 |
| VERMONT | 2020-03-08 | 50 | 852 | 136.3250 | 67.809 | d50 |
| FLORIDA | 2020-03-02 | 50 | 27865 | 135.2792 | 384.116 | d50 |
| IDAHO | 2020-03-14 | 50 | 2061 | 128.1913 | 24.250 | d50 |
| TENNESSEE | 2020-03-05 | 50 | 8449 | 127.2552 | 162.533 | d50 |
| KANSAS | 2020-03-08 | 50 | 3473 | 124.9866 | 47.448 | d50 |
| WISCONSIN | 2020-03-10 | 50 | 6520 | 114.7991 | 115.050 | d50 |
| SOUTH CAROLINA | 2020-03-07 | 50 | 5498 | 110.9379 | 164.864 | d50 |
| NEW HAMPSHIRE | 2020-03-02 | 50 | 1476 | 109.8523 | 150.081 | d50 |
| ARKANSAS | 2020-03-14 | 50 | 3263 | 109.7525 | 58.439 | d50 |
| MISSOURI | 2020-03-03 | 50 | 6068 | 103.5436 | 102.599 | d50 |
| WYOMING | 2020-03-12 | 50 | 566 | 99.9889 | 6.112 | d50 |
| KENTUCKY | 2020-03-06 | 50 | 3857 | 89.3957 | 117.581 | d50 |
| TEXAS | 2020-03-05 | 50 | 23772 | 85.7980 | 141.791 | d50 |
| MAINE | 2020-03-12 | 50 | 1123 | 84.2579 | 43.213 | d50 |
| OKLAHOMA | 2020-03-07 | 50 | 3254 | 84.1304 | 62.344 | d50 |
| NORTH CAROLINA | 2020-03-02 | 50 | 7331 | 72.8699 | 219.012 | d50 |
| WEST VIRGINIA | 2020-03-18 | 50 | 1287 | 71.6665 | 79.659 | d50 |
| MINNESOTA | 2020-03-06 | 50 | 3441 | 63.3649 | 76.483 | d50 |
| ALASKA | 2020-03-13 | 50 | 370 | 54.7374 | 2.197 | d50 |
| MONTANA | 2020-03-14 | 50 | 455 | 48.8130 | 11.928 | d50 |
| OREGON | 2020-02-29 | 50 | 1910 | 47.1890 | 56.429 | d50 |
| HAWAII | 2020-03-07 | 50 | 599 | 42.1251 | 221.812 | d50 |
| MASSACHUSETTS | 2020-01-29 | 50 | 1297 | 19.0685 | 888.905 | d50 |
| WASHINGTON | 2020-01-22 | 50 | 406 | 7.5113 | 293.038 | d50 |
| CALIFORNIA | 2020-01-26 | 50 | 557 | 1.5414 | 425.735 | d50 |
| ILLINOIS | 2020-01-24 | 50 | 64 | 0.7938 | 1652.305 | d50 |
| ARIZONA | 2020-01-26 | 50 | 18 | 0.3141 | 201.979 | d50 |
| NEW YORK | 2020-03-03 | 60 | 317174 | 1616.7126 | 416.294 | d60 |
| NEW JERSEY | 2020-03-05 | 60 | 127649 | 1437.1901 | 1207.721 | d60 |
| DISTRICT OF COLUMBIA | 2020-03-16 | 60 | 6871 | 1003.8013 | 11212.088 | d60 |
| CONNECTICUT | 2020-03-10 | 60 | 32738 | 914.0853 | 739.620 | d60 |
| RHODE ISLAND | 2020-03-01 | 60 | 7534 | 713.0344 | 1022.045 | d60 |
| LOUISIANA | 2020-03-11 | 60 | 31536 | 676.2135 | 107.944 | d60 |
| DELAWARE | 2020-03-11 | 60 | 6250 | 658.2446 | 487.283 | d60 |
| MICHIGAN | 2020-03-11 | 60 | 47798 | 481.3987 | 188.741 | d60 |
| MARYLAND | 2020-03-06 | 60 | 27117 | 451.6914 | 618.450 | d60 |
| SOUTH DAKOTA | 2020-03-11 | 60 | 3516 | 451.5676 | 16.393 | d60 |
| PENNSYLVANIA | 2020-03-06 | 60 | 53864 | 421.1026 | 285.883 | d60 |
| IOWA | 2020-03-09 | 60 | 11440 | 370.1450 | 58.196 | d60 |
| NEBRASKA | 2020-03-06 | 60 | 6373 | 346.9309 | 35.592 | d60 |
| MISSISSIPPI | 2020-03-12 | 60 | 9674 | 323.8231 | 64.232 | d60 |
| INDIANA | 2020-03-06 | 60 | 21033 | 316.8849 | 185.268 | d60 |
| COLORADO | 2020-03-05 | 60 | 16676 | 302.7777 | 58.793 | d60 |
| GEORGIA | 2020-03-03 | 60 | 26617 | 258.5980 | 180.022 | d60 |
| VIRGINIA | 2020-03-08 | 60 | 21570 | 257.3251 | 219.088 | d60 |
| UTAH | 2020-03-07 | 60 | 5178 | 235.0041 | 105.662 | d60 |
| NEW MEXICO | 2020-03-11 | 60 | 4844 | 232.7235 | 18.321 | d60 |
| KANSAS | 2020-03-08 | 60 | 6332 | 225.4592 | 44.901 | d60 |
| NORTH DAKOTA | 2020-03-12 | 60 | 1518 | 212.9782 | 13.314 | d60 |
| ALABAMA | 2020-03-11 | 60 | 9982 | 205.1934 | 96.054 | d60 |
| OHIO | 2020-03-10 | 60 | 23697 | 203.5496 | 284.916 | d60 |
| TENNESSEE | 2020-03-05 | 60 | 13091 | 197.1710 | 162.533 | d60 |
| NEVADA | 2020-03-05 | 60 | 5630 | 193.8497 | 34.132 | d60 |
| WISCONSIN | 2020-03-10 | 60 | 9939 | 173.8696 | 111.520 | d60 |
| NEW HAMPSHIRE | 2020-03-02 | 60 | 2295 | 170.8070 | 150.081 | d60 |
| FLORIDA | 2020-03-02 | 60 | 34720 | 168.5589 | 384.116 | d60 |
| VERMONT | 2020-03-08 | 60 | 912 | 145.9254 | 67.809 | d60 |
| MINNESOTA | 2020-03-06 | 60 | 7829 | 142.9426 | 74.743 | d60 |
| IDAHO | 2020-03-14 | 60 | 2293 | 142.6213 | 24.250 | d60 |
| SOUTH CAROLINA | 2020-03-07 | 60 | 6936 | 139.9537 | 164.864 | d60 |
| ARKANSAS | 2020-03-14 | 60 | 4096 | 137.7709 | 58.439 | d60 |
| MISSOURI | 2020-03-03 | 60 | 8022 | 136.8864 | 102.599 | d60 |
| KENTUCKY | 2020-03-06 | 60 | 5765 | 132.9791 | 116.119 | d60 |
| TEXAS | 2020-03-05 | 60 | 32889 | 118.5272 | 133.444 | d60 |
| WYOMING | 2020-03-12 | 60 | 669 | 118.1847 | 6.112 | d60 |
| NORTH CAROLINA | 2020-03-02 | 60 | 11585 | 114.4711 | 210.587 | d60 |
| MAINE | 2020-03-12 | 60 | 1456 | 109.2426 | 43.213 | d60 |
| MASSACHUSETTS | 2020-01-29 | 60 | 7410 | 108.9421 | 888.905 | d60 |
| OKLAHOMA | 2020-03-07 | 60 | 4202 | 108.3792 | 61.191 | d60 |
| WEST VIRGINIA | 2020-03-18 | 60 | 1492 | 82.3400 | 78.221 | d60 |
| OREGON | 2020-02-29 | 60 | 2446 | 60.3238 | 49.532 | d60 |
| ALASKA | 2020-03-13 | 60 | 390 | 56.9130 | 2.004 | d60 |
| MONTANA | 2020-03-14 | 60 | 462 | 50.1707 | 12.477 | d60 |
| HAWAII | 2020-03-07 | 60 | 616 | 43.3207 | 221.812 | d60 |
| WASHINGTON | 2020-01-22 | 60 | 1833 | 25.7746 | 141.238 | d60 |
| ILLINOIS | 2020-01-24 | 60 | 1537 | 13.8892 | 548.433 | d60 |
| CALIFORNIA | 2020-01-26 | 60 | 3910 | 10.1172 | 303.925 | d60 |
| ARIZONA | 2020-01-26 | 60 | 508 | 7.3797 | 64.339 | d60 |
| NEW YORK | 2020-03-03 | 70 | 342356 | 1745.0713 | 416.294 | d70 |
| NEW JERSEY | 2020-03-05 | 70 | 142581 | 1605.3084 | 1207.721 | d70 |
| DISTRICT OF COLUMBIA | 2020-03-16 | 70 | 8225 | 1201.6105 | 11212.088 | d70 |
| CONNECTICUT | 2020-03-10 | 70 | 38212 | 1066.9261 | 739.620 | d70 |
| DELAWARE | 2020-03-11 | 70 | 8146 | 857.9297 | 487.283 | d70 |
| RHODE ISLAND | 2020-03-01 | 70 | 8519 | 806.2570 | 1022.045 | d70 |
| LOUISIANA | 2020-03-11 | 70 | 35244 | 755.7226 | 107.944 | d70 |
| MARYLAND | 2020-03-06 | 70 | 36986 | 616.0806 | 618.450 | d70 |
| MICHIGAN | 2020-03-11 | 70 | 53878 | 542.6336 | 188.741 | d70 |
| SOUTH DAKOTA | 2020-03-11 | 70 | 4163 | 530.9346 | 15.966 | d70 |
| NEBRASKA | 2020-03-06 | 70 | 9610 | 516.7742 | 33.905 | d70 |
| PENNSYLVANIA | 2020-03-06 | 70 | 64136 | 501.4080 | 285.883 | d70 |
| IOWA | 2020-03-09 | 70 | 14946 | 479.1197 | 56.935 | d70 |
| MISSISSIPPI | 2020-03-12 | 70 | 12222 | 409.1136 | 64.232 | d70 |
| INDIANA | 2020-03-06 | 70 | 26656 | 401.6015 | 185.268 | d70 |
| COLORADO | 2020-03-05 | 70 | 20767 | 375.9274 | 56.001 | d70 |
| VIRGINIA | 2020-03-08 | 70 | 30388 | 362.2806 | 219.202 | d70 |
| MASSACHUSETTS | 2020-01-29 | 70 | 21955 | 322.7832 | 888.905 | d70 |
| GEORGIA | 2020-03-03 | 70 | 32207 | 312.7657 | 179.044 | d70 |
| NORTH DAKOTA | 2020-03-12 | 70 | 2229 | 309.0477 | 12.899 | d70 |
| NEW MEXICO | 2020-03-11 | 70 | 6307 | 302.3764 | 17.821 | d70 |
| UTAH | 2020-03-07 | 70 | 6489 | 294.5040 | 105.662 | d70 |
| KANSAS | 2020-03-08 | 70 | 7953 | 282.1729 | 44.144 | d70 |
| ALABAMA | 2020-03-11 | 70 | 13186 | 271.0559 | 96.054 | d70 |
| MINNESOTA | 2020-03-06 | 70 | 14216 | 258.5961 | 72.381 | d70 |
| OHIO | 2020-03-10 | 70 | 28952 | 248.6884 | 284.916 | d70 |
| TENNESSEE | 2020-03-05 | 70 | 16359 | 246.2035 | 162.011 | d70 |
| NEW HAMPSHIRE | 2020-03-02 | 70 | 3129 | 232.8780 | 150.081 | d70 |
| WISCONSIN | 2020-03-10 | 70 | 12885 | 224.5214 | 109.704 | d70 |
| NEVADA | 2020-03-05 | 70 | 6504 | 223.2423 | 30.352 | d70 |
| FLORIDA | 2020-03-02 | 70 | 40932 | 198.7170 | 384.116 | d70 |
| ARKANSAS | 2020-03-14 | 70 | 5627 | 188.4796 | 58.075 | d70 |
| SOUTH CAROLINA | 2020-03-07 | 70 | 8661 | 174.7605 | 164.864 | d70 |
| KENTUCKY | 2020-03-06 | 70 | 7413 | 169.9293 | 115.406 | d70 |
| IDAHO | 2020-03-14 | 70 | 2595 | 161.4053 | 24.250 | d70 |
| MISSOURI | 2020-03-03 | 70 | 9563 | 160.9854 | 100.439 | d70 |
| TEXAS | 2020-03-05 | 70 | 44701 | 160.9453 | 130.084 | d70 |
| NORTH CAROLINA | 2020-03-02 | 70 | 15601 | 153.8845 | 209.591 | d70 |
| VERMONT | 2020-03-08 | 70 | 935 | 149.6055 | 67.809 | d70 |
| MAINE | 2020-03-12 | 70 | 1874 | 140.6049 | 43.213 | d70 |
| WYOMING | 2020-03-12 | 70 | 801 | 139.3683 | 6.069 | d70 |
| OKLAHOMA | 2020-03-07 | 70 | 5237 | 134.6772 | 60.415 | d70 |
| WEST VIRGINIA | 2020-03-18 | 70 | 1899 | 104.8013 | 78.221 | d70 |
| OREGON | 2020-02-29 | 70 | 3160 | 77.6266 | 47.931 | d70 |
| WASHINGTON | 2020-01-22 | 70 | 5305 | 73.3638 | 115.674 | d70 |
| ILLINOIS | 2020-01-24 | 70 | 8489 | 69.4870 | 316.415 | d70 |
| ALASKA | 2020-03-13 | 70 | 418 | 59.7706 | 1.666 | d70 |
| MONTANA | 2020-03-14 | 70 | 479 | 52.0168 | 12.477 | d70 |
| HAWAII | 2020-03-07 | 70 | 629 | 44.2349 | 221.812 | d70 |
| CALIFORNIA | 2020-01-26 | 70 | 15040 | 38.5905 | 278.604 | d70 |
| ARIZONA | 2020-01-26 | 70 | 2269 | 32.6631 | 61.154 | d70 |
| NEW YORK | 2020-03-03 | 80 | 360818 | 1839.1766 | 416.294 | d80 |
| NEW JERSEY | 2020-03-05 | 80 | 154176 | 1735.8556 | 1207.721 | d80 |
| DISTRICT OF COLUMBIA | 2020-03-16 | 80 | 9120 | 1332.3633 | 11212.088 | d80 |
| CONNECTICUT | 2020-03-10 | 80 | 41536 | 1159.7362 | 739.620 | d80 |
| RHODE ISLAND | 2020-03-01 | 80 | 11800 | 1116.7781 | 1022.045 | d80 |
| DELAWARE | 2020-03-11 | 80 | 9370 | 986.8404 | 487.283 | d80 |
| LOUISIANA | 2020-03-11 | 80 | 39493 | 846.8322 | 107.944 | d80 |
| MARYLAND | 2020-03-06 | 80 | 47152 | 785.4170 | 618.450 | d80 |
| NEBRASKA | 2020-03-06 | 80 | 12173 | 652.3713 | 33.145 | d80 |
| MASSACHUSETTS | 2020-01-29 | 80 | 42486 | 624.6307 | 888.905 | d80 |
| SOUTH DAKOTA | 2020-03-11 | 80 | 4935 | 619.7545 | 15.043 | d80 |
| IOWA | 2020-03-09 | 80 | 18585 | 593.2963 | 56.081 | d80 |
| MICHIGAN | 2020-03-11 | 80 | 58001 | 584.1585 | 188.741 | d80 |
| PENNSYLVANIA | 2020-03-06 | 80 | 71925 | 562.3015 | 285.883 | d80 |
| MISSISSIPPI | 2020-03-12 | 80 | 15523 | 519.6098 | 64.232 | d80 |
| VIRGINIA | 2020-03-08 | 80 | 40249 | 479.8417 | 219.202 | d80 |
| INDIANA | 2020-03-06 | 80 | 31715 | 477.8208 | 185.268 | d80 |
| COLORADO | 2020-03-05 | 80 | 24049 | 435.3387 | 56.001 | d80 |
| MINNESOTA | 2020-03-06 | 80 | 21293 | 386.5513 | 72.182 | d80 |
| ALABAMA | 2020-03-11 | 80 | 17689 | 363.6210 | 96.054 | d80 |
| GEORGIA | 2020-03-03 | 80 | 37238 | 361.6223 | 179.044 | d80 |
| NORTH DAKOTA | 2020-03-12 | 80 | 2577 | 356.1783 | 12.777 | d80 |
| UTAH | 2020-03-07 | 80 | 7839 | 355.7738 | 105.662 | d80 |
| NEW MEXICO | 2020-03-11 | 80 | 7401 | 354.8260 | 17.821 | d80 |
| KANSAS | 2020-03-08 | 80 | 9291 | 327.1422 | 41.791 | d80 |
| WISCONSIN | 2020-03-10 | 80 | 17707 | 306.4346 | 106.695 | d80 |
| OHIO | 2020-03-10 | 80 | 34566 | 296.9108 | 284.916 | d80 |
| TENNESSEE | 2020-03-05 | 80 | 19697 | 296.1470 | 161.297 | d80 |
| NEW HAMPSHIRE | 2020-03-02 | 80 | 3920 | 291.7487 | 150.081 | d80 |
| NEVADA | 2020-03-05 | 80 | 7881 | 270.5062 | 30.352 | d80 |
| ARKANSAS | 2020-03-14 | 80 | 7615 | 255.0688 | 58.075 | d80 |
| FLORIDA | 2020-03-02 | 80 | 48593 | 235.9097 | 384.116 | d80 |
| NORTH CAROLINA | 2020-03-02 | 80 | 21802 | 214.6791 | 208.886 | d80 |
| SOUTH CAROLINA | 2020-03-07 | 80 | 10416 | 210.1727 | 164.864 | d80 |
| TEXAS | 2020-03-05 | 80 | 55961 | 201.1749 | 126.578 | d80 |
| KENTUCKY | 2020-03-06 | 80 | 8542 | 193.3893 | 113.476 | d80 |
| MISSOURI | 2020-03-03 | 80 | 11058 | 185.6679 | 99.435 | d80 |
| IDAHO | 2020-03-14 | 80 | 2933 | 179.9255 | 24.065 | d80 |
| MAINE | 2020-03-12 | 80 | 2323 | 174.2930 | 43.213 | d80 |
| ILLINOIS | 2020-01-24 | 80 | 21960 | 173.2347 | 254.062 | d80 |
| OKLAHOMA | 2020-03-07 | 80 | 6138 | 157.6200 | 60.021 | d80 |
| WYOMING | 2020-03-12 | 80 | 903 | 155.1984 | 5.993 | d80 |
| VERMONT | 2020-03-08 | 80 | 965 | 154.4057 | 67.809 | d80 |
| WASHINGTON | 2020-01-22 | 80 | 9586 | 131.4571 | 110.915 | d80 |
| WEST VIRGINIA | 2020-03-18 | 80 | 2136 | 117.8808 | 78.221 | d80 |
| OREGON | 2020-02-29 | 80 | 3726 | 91.5307 | 47.931 | d80 |
| ALASKA | 2020-03-13 | 80 | 491 | 69.2391 | 1.395 | d80 |
| CALIFORNIA | 2020-01-26 | 80 | 26699 | 68.3270 | 275.685 | d80 |
| ARIZONA | 2020-01-26 | 80 | 3964 | 57.0632 | 61.154 | d80 |
| MONTANA | 2020-03-14 | 80 | 523 | 56.2302 | 11.801 | d80 |
| HAWAII | 2020-03-07 | 80 | 633 | 44.5162 | 221.812 | d80 |
purl('workflow.rmd', output = "workflow.R")##
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|......................................................................| 100%## [1] "workflow.R"